{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/aprorok/Dropbox/Ceasefires Grant with Alyssa/LHF/Data & Analyses/Replication Files & Final Figures to Upload/BattlesBargains_ISQ_ReplicationLog.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 8 Jan 2021, 20:42:21

{com}. do "/var/folders/kt/j4m364t17zzgz_ckdqd2z19c0000gq/T//SD65428.000000"
{txt}
{com}. ***********************************************************************************
. ***************** Final Do File to produce all Tables/Figures for *****************
. *** Battles and Bargains: Escalation, Commitment, and Negotiations in Civil War ***
. ******************** Elizabeth J. Menninga & Alyssa K. Prorok *********************
. *************************** Analyses Run in Stata v. 15 ***************************
. ***********************************************************************************
. 
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. 
. **********************
. **********************
. *** Codebook Notes ***
. **********************
. **********************
. 
. * Main variable battles_escalation is a three category variable.
.         * higher values = greater escalation
.                 * battles_escalation = 0 (de-escalation post-negotiations)
.                 * battles_escalation = 1 (no change post-negotiations)
.                 * battles_escalation = 2 (escalation post-negotiations)
.                 
.         * Dummy versions (used in code below to make creating tables easier):
.                 * battles_escalation1 = de-escalation
.                 * battles_escalation2 = no change
.                 * battles_escalation3 = escalation
. 
. * for all variants on this variable, higher values always equal more escalation
. 
. 
. ********************************************
. ********************************************
. *** Descriptive Stats Referenced in Text ***
. ********************************************
. ********************************************
. 
. *** Dyad-months that experience negotiation (Thomas data)
. tab negotiations 

{txt}negotiation {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      3,731       89.43       89.43
{txt}          1 {c |}{res}        441       10.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      4,172      100.00
{txt}
{com}. 
. *** Dyads that experience negotiation (Thomas data)
. collapse (sum) negotiation, by(dyadid)
{txt}
{com}. tab negotiation

      {txt}(sum) {c |}
negotiation {c |}
          s {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         40       38.46       38.46
{txt}          1 {c |}{res}         14       13.46       51.92
{txt}          2 {c |}{res}         17       16.35       68.27
{txt}          3 {c |}{res}          7        6.73       75.00
{txt}          4 {c |}{res}          6        5.77       80.77
{txt}          5 {c |}{res}          3        2.88       83.65
{txt}          6 {c |}{res}          2        1.92       85.58
{txt}          7 {c |}{res}          1        0.96       86.54
{txt}          8 {c |}{res}          1        0.96       87.50
{txt}          9 {c |}{res}          1        0.96       88.46
{txt}         11 {c |}{res}          1        0.96       89.42
{txt}         16 {c |}{res}          1        0.96       90.38
{txt}         17 {c |}{res}          3        2.88       93.27
{txt}         19 {c |}{res}          1        0.96       94.23
{txt}         20 {c |}{res}          1        0.96       95.19
{txt}         23 {c |}{res}          1        0.96       96.15
{txt}         25 {c |}{res}          1        0.96       97.12
{txt}         26 {c |}{res}          1        0.96       98.08
{txt}         47 {c |}{res}          1        0.96       99.04
{txt}         59 {c |}{res}          1        0.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        104      100.00
{txt}
{com}. 
. *** Footnote 6: Bivariate relationship between political and battlefield outcomes
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. tab weak_con_last_neg_dum battles_escalation if t==2, chi2 exact

{text}Enumerating sample-space combinations:
{text}stage 3:{col 11}enumerations = 1
{text}stage 2:{col 11}enumerations = 3
{text}stage 1:{col 11}enumerations = 0

     Prior {c |}
Concession {c |} Post-Negotiation (De-)Escalation
         s {c |}         0          1          2 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}        57         28         41 {txt}{c |}{res}       126 
{txt}         1 {c |}{res}        27         10         20 {txt}{c |}{res}        57 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        84         38         61 {txt}{c |}{res}       183 

{txt}          Pearson chi2({res}2{txt}) = {res}  0.5289  {txt} Pr = {res}0.768
{txt}           Fisher's exact =                 {res}0.800
{txt}
{com}. 
. *** Footnote 11: Frequency of third-party intervention
. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. * Dyad-months that experience third-party intervention
. tab thirdparty_involvement if e(sample)

{txt}Third-Party {c |}
Involvement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        741       62.85       62.85
{txt}          1 {c |}{res}        438       37.15      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,179      100.00
{txt}
{com}. * Dyads that experience third-party intervention at some point
. keep if e(sample)
{txt}(2,688 observations deleted)

{com}. collapse (sum) thirdparty_involvement, by(dyadid dyad_startyr)
{txt}
{com}. tab thirdparty_involvement

      {txt}(sum) {c |}
thirdparty_ {c |}
involvement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         12       28.57       28.57
{txt}          1 {c |}{res}          3        7.14       35.71
{txt}          2 {c |}{res}          6       14.29       50.00
{txt}          3 {c |}{res}          2        4.76       54.76
{txt}          5 {c |}{res}          1        2.38       57.14
{txt}          6 {c |}{res}          2        4.76       61.90
{txt}          7 {c |}{res}          2        4.76       66.67
{txt}          8 {c |}{res}          1        2.38       69.05
{txt}          9 {c |}{res}          2        4.76       73.81
{txt}         10 {c |}{res}          1        2.38       76.19
{txt}         20 {c |}{res}          1        2.38       78.57
{txt}         22 {c |}{res}          1        2.38       80.95
{txt}         23 {c |}{res}          1        2.38       83.33
{txt}         24 {c |}{res}          1        2.38       85.71
{txt}         26 {c |}{res}          1        2.38       88.10
{txt}         28 {c |}{res}          1        2.38       90.48
{txt}         37 {c |}{res}          1        2.38       92.86
{txt}         40 {c |}{res}          1        2.38       95.24
{txt}         63 {c |}{res}          1        2.38       97.62
{txt}         67 {c |}{res}          1        2.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         42      100.00
{txt}
{com}. 
. *** Number of negotiation spells by dyad 
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. collapse (sum) neg_onset, by(dyadid dyad_startyr)
{txt}
{com}. tab neg_onset if dyad_startyr>1989

      {txt}(sum) {c |}
  neg_onset {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         34       39.08       39.08
{txt}          1 {c |}{res}         21       24.14       63.22
{txt}          2 {c |}{res}         14       16.09       79.31
{txt}          3 {c |}{res}          2        2.30       81.61
{txt}          4 {c |}{res}          6        6.90       88.51
{txt}          5 {c |}{res}          1        1.15       89.66
{txt}          6 {c |}{res}          2        2.30       91.95
{txt}          7 {c |}{res}          1        1.15       93.10
{txt}          8 {c |}{res}          1        1.15       94.25
{txt}          9 {c |}{res}          1        1.15       95.40
{txt}         10 {c |}{res}          2        2.30       97.70
{txt}         11 {c |}{res}          1        1.15       98.85
{txt}         16 {c |}{res}          1        1.15      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         87      100.00
{txt}
{com}. sum neg_onset if dyad_startyr>1989 & neg_onset>1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}neg_onset {c |}{res}         32      4.5625     3.46352          2         16
{txt}
{com}. 
. *** Average time between negotiations
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. sum t if neg_onset==1 & e(sample), d

           {txt}Time Since Previous Negotiations Ended
{hline 61}
      Percentiles      Smallest
 1%    {res}        2              2
{txt} 5%    {res}        2              2
{txt}10%    {res}        2              2       {txt}Obs         {res}        142
{txt}25%    {res}        2              2       {txt}Sum of Wgt. {res}        142

{txt}50%    {res}        4                      {txt}Mean          {res} 7.584507
                        {txt}Largest       Std. Dev.     {res} 9.378747
{txt}75%    {res}        9             41
{txt}90%    {res}       16             42       {txt}Variance      {res} 87.96089
{txt}95%    {res}       26             51       {txt}Skewness      {res} 2.988596
{txt}99%    {res}       51             59       {txt}Kurtosis      {res}  13.4275
{txt}
{com}. tab t if neg_onset==1 & e(sample)

 {txt}Time Since {c |}
   Previous {c |}
Negotiation {c |}
    s Ended {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}         43       30.28       30.28
{txt}          3 {c |}{res}         25       17.61       47.89
{txt}          4 {c |}{res}         13        9.15       57.04
{txt}          5 {c |}{res}          9        6.34       63.38
{txt}          6 {c |}{res}          4        2.82       66.20
{txt}          7 {c |}{res}          5        3.52       69.72
{txt}          8 {c |}{res}          6        4.23       73.94
{txt}          9 {c |}{res}          3        2.11       76.06
{txt}         10 {c |}{res}          4        2.82       78.87
{txt}         11 {c |}{res}          1        0.70       79.58
{txt}         12 {c |}{res}          3        2.11       81.69
{txt}         13 {c |}{res}          3        2.11       83.80
{txt}         14 {c |}{res}          1        0.70       84.51
{txt}         15 {c |}{res}          5        3.52       88.03
{txt}         16 {c |}{res}          4        2.82       90.85
{txt}         17 {c |}{res}          1        0.70       91.55
{txt}         18 {c |}{res}          2        1.41       92.96
{txt}         19 {c |}{res}          1        0.70       93.66
{txt}         21 {c |}{res}          1        0.70       94.37
{txt}         26 {c |}{res}          1        0.70       95.07
{txt}         27 {c |}{res}          1        0.70       95.77
{txt}         32 {c |}{res}          1        0.70       96.48
{txt}         35 {c |}{res}          1        0.70       97.18
{txt}         41 {c |}{res}          1        0.70       97.89
{txt}         42 {c |}{res}          1        0.70       98.59
{txt}         51 {c |}{res}          1        0.70       99.30
{txt}         59 {c |}{res}          1        0.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        142      100.00
{txt}
{com}.          
. *** Number of observations, dyads, countries
. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. codebook dyadid sidea if e(sample)

{txt}{hline}
{res}dyadid{right:DyadId}
{txt}{hline}

{col 19}type:  numeric ({res}long{txt})

{col 18}range:  [{res}1{txt},{res}749{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}42{col 51}{txt}missing .:  {res}0{txt}/{res}1,484

{txt}{col 19}mean:{res}{col 26} 160.937
{txt}{col 15}std. dev:{res}{col 26} 203.604

{txt}{col 12}percentiles:{col 32}10%{col 42}25%{col 52}50%{col 62}75%{col 72}90%
{res}{col 27}       7{col 37}      24{col 47}      55{col 57}     190{col 67}     641

{txt}{hline}
{res}sidea{right:Side A}
{txt}{hline}

{col 19}type:  string ({res}str36{txt})

{col 10}unique values:  {res}18{col 51}{txt}missing "":  {res}0{txt}/{res}1,484

{txt}{col 15}examples:  {res}"Burundi"
{col 26}"Ethiopia"
{col 26}"Mozambique"
{col 26}"Sudan"

{txt}{col 16}warning:  variable has embedded blanks

{com}. 
. *** Dyad-months that experience negotiation onset
. tab neg_DV if e(sample)

{txt}Negotiation {c |}
      Onset {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,342       90.43       90.43
{txt}          1 {c |}{res}        142        9.57      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,484      100.00
{txt}
{com}. 
. *** Footnote 17: Avg battles during negotiations v outside negotiations
. sum battleonset_monthtotal if negotiations==1 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
battleonse~l {c |}{res}        441    3.535147    6.729465          0         67
{txt}
{com}. sum battleonset_monthtotal if negotiations==0 & battles_escalation!=.

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
battleonse~l {c |}{res}      1,949    2.434069    4.739561          0         33
{txt}
{com}. 
. corr l2.battleonset_monthtotal battles_escalation if t==2 & e(sample)
{txt}(obs=163)

             {c |}       L2.         
             {c |} battle~l battle~n
{hline 13}{c +}{hline 18}
battleonse~l {c |}
         L2. {c |}{res}   1.0000
{txt}battles_es~n {c |}{res}   0.0867   1.0000

{txt}
{com}.         
. *** Percent of negotiations that (de-)escalate
. tab battles_escalation if e(sample) & t==2

{txt}Post-Negoti {c |}
      ation {c |}
(De-)Escala {c |}
       tion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         74       45.40       45.40
{txt}          1 {c |}{res}         31       19.02       64.42
{txt}          2 {c |}{res}         58       35.58      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        163      100.00
{txt}
{com}. 
. *** Percent of dyad-months that (de-)escalate
. tab battles_escalation if e(sample)

{txt}Post-Negoti {c |}
      ation {c |}
(De-)Escala {c |}
       tion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        434       29.25       29.25
{txt}          1 {c |}{res}        439       29.58       58.83
{txt}          2 {c |}{res}        611       41.17      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,484      100.00
{txt}
{com}. 
. 
. 
. 
. *******************************************
. *******************************************
. *** Figure 1: Time Series for Sudan-NDA ***
. *******************************************
. *******************************************
. gen battles_escalation_plot=battles_escalation if dyadid==645
{txt}(4,145 missing values generated)

{com}. replace battles_escalation_plot=-.05 if battles_escalation_plot==0
{txt}(14 real changes made)

{com}. replace battles_escalation_plot=2.05 if battles_escalation_plot==2
{txt}(13 real changes made)

{com}. graph twoway (tsline battleonset_monthtotal battles_escalation_plot, lcolor(black black) lpattern(longdash solid) cmissing(n n)) if dyadid==645 & date>=460 & date<=500, ///
>      xline(472.5, lwidth(9) lc(gs12)) ///
>          xline(471, lc(black) lpattern(dash)) ///
>      xline(474, lc(black)) ///
>      xline(487.5, lwidth(9) lc(gs12)) ///
>          xline(486, lc(black) lpattern(dash)) ///
>      xline(489, lc(black)) ///
>          xtitle(" ") ///
>          ytitle("Number of Battle Onsets" " ") ///
>      xlabel(460 "May 1998" 465 "Oct. 1998" 470 "Mar. 1999" 475 "Aug. 1999" 480 "Jan. 2000" 485 "June 2000" 490 "Nov. 2000" 495 "Apr. 2001" 500 "Sept. 2001", angle(45)) ///
>      title("Figure 1: Negotiations and (De-)Escalation in the Sudan-NDA Dyad", size(med) span) ///
>      subtitle(" ", size(medsmall)) ///
>          graphregion(color(white)) ///
>          legend(off) ///
>          saving(Sudan-NDA.gph, replace)
{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(file Sudan-NDA.gph saved)

{com}.          
. 
.          
. ********************************************
. ********************************************
. *** Descriptive Stats Referenced in Text ***
. ********************************************
. ********************************************
. 
. *** Dyad-months with third-party involvement
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. tab thirdparty_involvement if e(sample)

{txt}Third-Party {c |}
Involvement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        741       62.85       62.85
{txt}          1 {c |}{res}        438       37.15      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,179      100.00
{txt}
{com}.          
.          
.          
. **********************************
. **********************************
. *** Table 1: Descriptive Stats ***
. **********************************
. **********************************
. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. asdoc sum neg_DV battles_escalation1 battles_escalation2 battles_escalation3 thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample) ///
>   , title(Table 1: Descriptive Statistics for Key Variables and Controls) ///
>   fhc(\b) ///
>   label ///
>   stat(N min max mean p50 sd) ///
>   dec(2) 
{txt}(File Myfile.doc already exists, option {bf:append} was assumed)
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:N}{space 1}{space 1}{ralign 9:min}{space 1}{space 1}{ralign 9:max}{space 1}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:p50}{space 1}{space 1}{ralign 9:sd}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:neg_DV}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .0956873}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .2942611}}}{space 1}
{space 0}{space 0}{ralign 12:battles_es~1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .2924528}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4550425}}}{space 1}
{space 0}{space 0}{ralign 12:battles_es~2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .2958221}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4565653}}}{space 1}
{space 0}{space 0}{ralign 12:battles_es~3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .4117251}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4923117}}}{space 1}
{space 0}{space 0}{ralign 12:thirdparty~t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1179}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .3715013}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .4834111}}}{space 1}
{space 0}{space 0}{ralign 12:weak_con_l~m}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .1981132}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .3987123}}}{space 1}
{space 0}{space 0}{ralign 12:str_centcomm}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        3}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.955526}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf: .8008192}}}{space 1}
{space 0}{space 0}{ralign 12:parity}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf: .0801887}}}{space 1}{space 1}{ralign 9:{res:{sf:        0}}}{space 1}{space 1}{ralign 9:{res:{sf: .2716766}}}{space 1}
{space 0}{space 0}{ralign 12:groupnum}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        1}}}{space 1}{space 1}{ralign 9:{res:{sf:        4}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.670485}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf: .7120328}}}{space 1}
{space 0}{space 0}{ralign 12:dyad_months}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        6}}}{space 1}{space 1}{ralign 9:{res:{sf:      252}}}{space 1}{space 1}{ralign 9:{res:{sf:  87.9535}}}{space 1}{space 1}{ralign 9:{res:{sf:       74}}}{space 1}{space 1}{ralign 9:{res:{sf: 60.06595}}}{space 1}
{space 0}{space 0}{ralign 12:t}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     1484}}}{space 1}{space 1}{ralign 9:{res:{sf:        2}}}{space 1}{space 1}{ralign 9:{res:{sf:      205}}}{space 1}{space 1}{ralign 9:{res:{sf: 29.53571}}}{space 1}{space 1}{ralign 9:{res:{sf:       12}}}{space 1}{space 1}{ralign 9:{res:{sf: 41.19045}}}{space 1}
{res}Click to Open File:  {browse "Myfile.doc"}
{txt}
{com}.   
.   
.   
.   
.   
. *** Footnote 25: LRT Time-Varying Effects 
. logit neg_DV ib0.battles_escalation##c.t ib0.battles_escalation##c.t##c.t ib0.battles_escalation##c.t##c.t##c.t c.t##c.t c.t##c.t##c.t weak_con_last_neg_dum str_centcomm parity groupnum dyad_months 

{txt}note: t omitted because of collinearity
note: t omitted because of collinearity
note: t omitted because of collinearity
note: t omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log likelihood = {res:-468.20555}  
Iteration 1:{space 3}log likelihood = {res:-420.62359}  
Iteration 2:{space 3}log likelihood = {res:-407.09481}  
Iteration 3:{space 3}log likelihood = {res: -405.2191}  
Iteration 4:{space 3}log likelihood = {res:-404.28515}  
Iteration 5:{space 3}log likelihood = {res: -403.8197}  
Iteration 6:{space 3}log likelihood = {res:-403.40536}  
Iteration 7:{space 3}log likelihood = {res:-403.09025}  
Iteration 8:{space 3}log likelihood = {res:-403.01311}  
Iteration 9:{space 3}log likelihood = {res:-403.00991}  
Iteration 10:{space 2}log likelihood = {res: -403.0099}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}LR chi2({res}16{txt}){col 67}= {res}    130.39
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res} -403.0099{txt}{col 49}Pseudo R2{col 67}= {res}    0.1392

{txt}{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2} .0297443{col 40}{space 2} .5779556{col 51}{space 1}    0.05{col 60}{space 3}0.959{col 68}{space 4}-1.103028{col 81}{space 3} 1.162517
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.0113614{col 40}{space 2} .5142707{col 51}{space 1}   -0.02{col 60}{space 3}0.982{col 68}{space 4}-1.019313{col 81}{space 3} .9965906
{txt}{space 26} {c |}
{space 25}t {c |}{col 28}{res}{space 2}-.1510465{col 40}{space 2} .0984508{col 51}{space 1}   -1.53{col 60}{space 3}0.125{col 68}{space 4}-.3440065{col 81}{space 3} .0419135
{txt}{space 26} {c |}
{space 4}battles_escalation#c.t {c |}
{space 24}1  {c |}{col 28}{res}{space 2}-.0804723{col 40}{space 2} .1395695{col 51}{space 1}   -0.58{col 60}{space 3}0.564{col 68}{space 4}-.3540234{col 81}{space 3} .1930788
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.1204527{col 40}{space 2} .1370313{col 51}{space 1}   -0.88{col 60}{space 3}0.379{col 68}{space 4}-.3890292{col 81}{space 3} .1481238
{txt}{space 26} {c |}
{space 25}t {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0054212{col 40}{space 2} .0058087{col 51}{space 1}    0.93{col 60}{space 3}0.351{col 68}{space 4}-.0059636{col 81}{space 3} .0168061
{txt}{space 26} {c |}
battles_escalation#c.t#c.t {c |}
{space 24}1  {c |}{col 28}{res}{space 2} .0004702{col 40}{space 2} .0069541{col 51}{space 1}    0.07{col 60}{space 3}0.946{col 68}{space 4}-.0131595{col 81}{space 3} .0140999
{txt}{space 24}2  {c |}{col 28}{res}{space 2} .0046485{col 40}{space 2} .0076759{col 51}{space 1}    0.61{col 60}{space 3}0.545{col 68}{space 4} -.010396{col 81}{space 3} .0196931
{txt}{space 26} {c |}
{space 25}t {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000724{col 40}{space 2} .0000888{col 51}{space 1}   -0.82{col 60}{space 3}0.415{col 68}{space 4}-.0002466{col 81}{space 3} .0001017
{txt}{space 26} {c |}
{space 8}battles_escalation#{c |}
{space 15}c.t#c.t#c.t {c |}
{space 24}1  {c |}{col 28}{res}{space 2}  .000025{col 40}{space 2}  .000097{col 51}{space 1}    0.26{col 60}{space 3}0.796{col 68}{space 4}-.0001651{col 81}{space 3} .0002152
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.0000412{col 40}{space 2} .0001126{col 51}{space 1}   -0.37{col 60}{space 3}0.715{col 68}{space 4}-.0002619{col 81}{space 3} .0001796
{txt}{space 26} {c |}
{space 25}t {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 25}t {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .5014521{col 40}{space 2} .2032996{col 51}{space 1}    2.47{col 60}{space 3}0.014{col 68}{space 4} .1029921{col 81}{space 3}  .899912
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.0192267{col 40}{space 2} .1260967{col 51}{space 1}   -0.15{col 60}{space 3}0.879{col 68}{space 4}-.2663717{col 81}{space 3} .2279184
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .5342936{col 40}{space 2} .2949536{col 51}{space 1}    1.81{col 60}{space 3}0.070{col 68}{space 4}-.0438049{col 81}{space 3} 1.112392
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}  .036941{col 40}{space 2} .1345282{col 51}{space 1}    0.27{col 60}{space 3}0.784{col 68}{space 4}-.2267294{col 81}{space 3} .3006115
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0014193{col 40}{space 2} .0021101{col 51}{space 1}   -0.67{col 60}{space 3}0.501{col 68}{space 4}-.0055549{col 81}{space 3} .0027164
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}-1.002627{col 40}{space 2} .5204493{col 51}{space 1}   -1.93{col 60}{space 3}0.054{col 68}{space 4}-2.022689{col 81}{space 3} .0174347
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 114 failures and 0 successes completely determined.{p_end}

{com}. estimates store m1r1c
{txt}
{com}. logit neg_DV ib0.battles_escalation c.t##c.t c.t##c.t##c.t weak_con_last_neg_dum str_centcomm parity groupnum dyad_months 

{txt}note: t omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log likelihood = {res:-468.20555}  
Iteration 1:{space 3}log likelihood = {res:-419.55494}  
Iteration 2:{space 3}log likelihood = {res:-408.60197}  
Iteration 3:{space 3}log likelihood = {res:-407.62799}  
Iteration 4:{space 3}log likelihood = {res:-407.11735}  
Iteration 5:{space 3}log likelihood = {res:-406.27401}  
Iteration 6:{space 3}log likelihood = {res:-405.25585}  
Iteration 7:{space 3}log likelihood = {res:-404.80807}  
Iteration 8:{space 3}log likelihood = {res:-404.74966}  
Iteration 9:{space 3}log likelihood = {res:-404.74907}  
Iteration 10:{space 2}log likelihood = {res:-404.74907}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}LR chi2({res}10{txt}){col 67}= {res}    126.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-404.74907{txt}{col 49}Pseudo R2{col 67}= {res}    0.1355

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.4192942{col 35}{space 2} .2603198{col 46}{space 1}   -1.61{col 55}{space 3}0.107{col 63}{space 4}-.9295117{col 76}{space 3} .0909233
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.4683414{col 35}{space 2} .2118875{col 46}{space 1}   -2.21{col 55}{space 3}0.027{col 63}{space 4}-.8836332{col 76}{space 3}-.0530496
{txt}{space 21} {c |}
{space 20}t {c |}{col 23}{res}{space 2}-.1895825{col 35}{space 2} .0527203{col 46}{space 1}   -3.60{col 55}{space 3}0.000{col 63}{space 4}-.2929124{col 76}{space 3}-.0862526
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0056347{col 35}{space 2} .0025688{col 46}{space 1}    2.19{col 55}{space 3}0.028{col 63}{space 4} .0005999{col 76}{space 3} .0106695
{txt}{space 21} {c |}
{space 20}t {c |}{col 23}{res}{space 2}        0{col 35}{txt}  (omitted)
{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000562{col 35}{space 2} .0000325{col 46}{space 1}   -1.73{col 55}{space 3}0.084{col 63}{space 4}-.0001199{col 76}{space 3} 7.54e-06
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4653051{col 35}{space 2} .1993805{col 46}{space 1}    2.33{col 55}{space 3}0.020{col 63}{space 4} .0745266{col 76}{space 3} .8560836
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0029436{col 35}{space 2} .1245678{col 46}{space 1}    0.02{col 55}{space 3}0.981{col 63}{space 4}-.2412047{col 76}{space 3}  .247092
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5161277{col 35}{space 2} .2938211{col 46}{space 1}    1.76{col 55}{space 3}0.079{col 63}{space 4}-.0597512{col 76}{space 3} 1.092007
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}  .046643{col 35}{space 2} .1335339{col 46}{space 1}    0.35{col 55}{space 3}0.727{col 63}{space 4}-.2150787{col 76}{space 3} .3083647
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0012904{col 35}{space 2} .0021027{col 46}{space 1}   -0.61{col 55}{space 3}0.539{col 63}{space 4}-.0054116{col 76}{space 3} .0028307
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}-.8649248{col 35}{space 2} .4443544{col 46}{space 1}   -1.95{col 55}{space 3}0.052{col 63}{space 4}-1.735843{col 76}{space 3} .0059938
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 112 failures and 0 successes completely determined.{p_end}

{com}. estimates store m1r1c2
{txt}
{com}. lrtest m1r1c m1r1c2 

{txt}Likelihood-ratio test{col 55}LR chi2({res}6{txt}){col 67}={res}      3.48
{txt}(Assumption: {res}{stata est replay m1r1c2:m1r1c2}{txt} nested in {res}{stata est replay m1r1c:m1r1c}{txt}){col 55}Prob > chi2 = {res}   0.7468
{txt}
{com}.   
.   
. 
. ******************************
. ******************************
. *** Table 2: Main Analyses ***
. ******************************
. ******************************
. * Model 1, Table 2
. logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-468.20555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-419.55494}  
Iteration 2:{space 3}log pseudolikelihood = {res:-408.60197}  
Iteration 3:{space 3}log pseudolikelihood = {res:-407.62799}  
Iteration 4:{space 3}log pseudolikelihood = {res:-407.11735}  
Iteration 5:{space 3}log pseudolikelihood = {res:-406.27401}  
Iteration 6:{space 3}log pseudolikelihood = {res:-405.25585}  
Iteration 7:{space 3}log pseudolikelihood = {res:-404.80807}  
Iteration 8:{space 3}log pseudolikelihood = {res:-404.74966}  
Iteration 9:{space 3}log pseudolikelihood = {res:-404.74907}  
Iteration 10:{space 2}log pseudolikelihood = {res:-404.74907}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    123.13
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-404.74907{txt}{col 49}Pseudo R2{col 67}= {res}    0.1355

{txt}{ralign 87:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.4192942{col 35}{space 2} .2370674{col 46}{space 1}   -1.77{col 55}{space 3}0.077{col 63}{space 4}-.8839377{col 76}{space 3} .0453493
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.4683414{col 35}{space 2} .1627205{col 46}{space 1}   -2.88{col 55}{space 3}0.004{col 63}{space 4}-.7872677{col 76}{space 3}-.1494152
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4653051{col 35}{space 2} .1541447{col 46}{space 1}    3.02{col 55}{space 3}0.003{col 63}{space 4} .1631871{col 76}{space 3} .7674232
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0029436{col 35}{space 2} .1143865{col 46}{space 1}    0.03{col 55}{space 3}0.979{col 63}{space 4}-.2212499{col 76}{space 3} .2271372
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5161277{col 35}{space 2}  .175183{col 46}{space 1}    2.95{col 55}{space 3}0.003{col 63}{space 4} .1727753{col 76}{space 3} .8594801
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}  .046643{col 35}{space 2} .1497159{col 46}{space 1}    0.31{col 55}{space 3}0.755{col 63}{space 4}-.2467947{col 76}{space 3} .3400807
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0012904{col 35}{space 2} .0026083{col 46}{space 1}   -0.49{col 55}{space 3}0.621{col 63}{space 4}-.0064026{col 76}{space 3} .0038218
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1895825{col 35}{space 2} .0467585{col 46}{space 1}   -4.05{col 55}{space 3}0.000{col 63}{space 4}-.2812274{col 76}{space 3}-.0979375
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0056347{col 35}{space 2} .0023312{col 46}{space 1}    2.42{col 55}{space 3}0.016{col 63}{space 4} .0010657{col 76}{space 3} .0102037
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000562{col 35}{space 2}  .000028{col 46}{space 1}   -2.01{col 55}{space 3}0.045{col 63}{space 4} -.000111{col 76}{space 3}-1.35e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.8649248{col 35}{space 2} .4949035{col 46}{space 1}   -1.75{col 55}{space 3}0.081{col 63}{space 4}-1.834918{col 76}{space 3} .1050682
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 112 failures and 0 successes completely determined.{p_end}

{com}. estimates store m1
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay m1:m1}}{col 14}{c |}{res}{col 16}     1,484{col 27}-468.2056{col 38}-404.7491{col 49}    11{col 58} 831.4981{col 69} 889.8256
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.818
{txt}2-fold (N=148).........AUC ={res}  0.738
{txt}3-fold (N=149).........AUC ={res}  0.734
{txt}4-fold (N=148).........AUC ={res}  0.781
{txt}5-fold (N=148).........AUC ={res}  0.632
{txt}6-fold (N=149).........AUC ={res}  0.721
{txt}7-fold (N=148).........AUC ={res}  0.738
{txt}8-fold (N=149).........AUC ={res}  0.748
{txt}9-fold (N=148).........AUC ={res}  0.842
{txt}10-fold (N=148).........AUC ={res}  0.769

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7521
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7062, 0.7848
{txt}cvSD AUC:                        {c |}{res} 0.0574
{txt}{hline 64}

{com}. 
. * Model 2, Table 2
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-320.29006}  
Iteration 2:{space 3}log pseudolikelihood = {res:-306.32969}  
Iteration 3:{space 3}log pseudolikelihood = {res: -305.3572}  
Iteration 4:{space 3}log pseudolikelihood = {res:-304.75165}  
Iteration 5:{space 3}log pseudolikelihood = {res:-303.92628}  
Iteration 6:{space 3}log pseudolikelihood = {res:-303.19976}  
Iteration 7:{space 3}log pseudolikelihood = {res:-303.01368}  
Iteration 8:{space 3}log pseudolikelihood = {res:  -303.006}  
Iteration 9:{space 3}log pseudolikelihood = {res:-303.00598}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    167.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-303.00598{txt}{col 49}Pseudo R2{col 67}= {res}    0.1764

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.3238621{col 38}{space 2}  .484835{col 49}{space 1}   -0.67{col 58}{space 3}0.504{col 66}{space 4}-1.274121{col 79}{space 3}  .626397
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.281887{col 38}{space 2} .4139636{col 49}{space 1}   -3.10{col 58}{space 3}0.002{col 66}{space 4} -2.09324{col 79}{space 3}-.4705329
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .6292977{col 38}{space 2} .5296279{col 49}{space 1}    1.19{col 58}{space 3}0.235{col 66}{space 4} -.408754{col 79}{space 3} 1.667349
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .2502056{col 38}{space 2} .6817039{col 49}{space 1}    0.37{col 58}{space 3}0.714{col 66}{space 4}-1.085909{col 79}{space 3} 1.586321
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.276698{col 38}{space 2} .5074873{col 49}{space 1}    2.52{col 58}{space 3}0.012{col 66}{space 4} .2820413{col 79}{space 3} 2.271355
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4433578{col 38}{space 2} .2071524{col 49}{space 1}    2.14{col 58}{space 3}0.032{col 66}{space 4} .0373464{col 79}{space 3} .8493691
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0990786{col 38}{space 2} .0961576{col 49}{space 1}   -1.03{col 58}{space 3}0.303{col 66}{space 4}-.2875441{col 79}{space 3} .0893869
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5090594{col 38}{space 2} .1938726{col 49}{space 1}    2.63{col 58}{space 3}0.009{col 66}{space 4} .1290762{col 79}{space 3} .8890427
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1024433{col 38}{space 2} .1749078{col 49}{space 1}   -0.59{col 58}{space 3}0.558{col 66}{space 4}-.4452564{col 79}{space 3} .2403697
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0008678{col 38}{space 2} .0022405{col 49}{space 1}   -0.39{col 58}{space 3}0.699{col 66}{space 4} -.005259{col 79}{space 3} .0035234
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1755696{col 38}{space 2} .0532312{col 49}{space 1}   -3.30{col 58}{space 3}0.001{col 66}{space 4}-.2799008{col 79}{space 3}-.0712384
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0054399{col 38}{space 2} .0025243{col 49}{space 1}    2.16{col 58}{space 3}0.031{col 66}{space 4} .0004924{col 79}{space 3} .0103875
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2} -.000053{col 38}{space 2} .0000283{col 49}{space 1}   -1.87{col 58}{space 3}0.062{col 66}{space 4}-.0001085{col 79}{space 3} 2.57e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.9683115{col 38}{space 2} .6278188{col 49}{space 1}   -1.54{col 58}{space 3}0.123{col 66}{space 4}-2.198814{col 79}{space 3} .2621907
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 81 failures and 0 successes completely determined.{p_end}

{com}. estimates store m2
{txt}
{com}. estat ic

Akaike's information criterion and Bayesian information criterion

{txt}{hline 13}{c TT}{hline 63}
       Model {c |}        Obs  ll(null)  ll(model)      df         AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:{stata estimates replay m2:m2}}{col 14}{c |}{res}{col 16}     1,179{col 27}-367.8836{col 38} -303.006{col 49}    14{col 58}  634.012{col 69} 705.0259
{txt}{hline 13}{c BT}{hline 63}
{p 15 21 2}
Note: N=Obs used in calculating BIC; see {helpb bic_note:[R] BIC note}.
{p_end}

{com}. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.721
{txt}2-fold (N=118).........AUC ={res}  0.854
{txt}3-fold (N=118).........AUC ={res}  0.744
{txt}4-fold (N=118).........AUC ={res}  0.660
{txt}5-fold (N=118).........AUC ={res}  0.794
{txt}6-fold (N=118).........AUC ={res}  0.815
{txt}7-fold (N=118).........AUC ={res}  0.808
{txt}8-fold (N=118).........AUC ={res}  0.820
{txt}9-fold (N=118).........AUC ={res}  0.775
{txt}10-fold (N=117).........AUC ={res}  0.774

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7767
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7352, 0.8141
{txt}cvSD AUC:                        {c |}{res} 0.0561
{txt}{hline 64}

{com}. 
. * Create Table 2 *
. #delimit;
{txt}delimiter now ;
{com}. esttab m1 m2 using table2.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table 2: Logistic Regression Results) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. *p<0.05.) 
>   nonumbers mtitles("Model 1" "Model 2")  
>   star(* 0.05) ;
{res}{txt}(output written to {browse  `"table2.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
. 
. *******************************************************************
. *******************************************************************
. *** Figure 2: Predicted Probabilities based on Model 1, Table 2 ***
. *******************************************************************
. *******************************************************************
. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. margins battles_escalation, vsquish  
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,484
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(neg_DV), predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32} Delta-method
{col 20}{c |}     Margin{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation {c |}
{space 16}0  {c |}{col 20}{res}{space 2} .1189213{col 32}{space 2}  .010895{col 43}{space 1}   10.92{col 52}{space 3}0.000{col 60}{space 4} .0975675{col 73}{space 3} .1402752
{txt}{space 16}1  {c |}{col 20}{res}{space 2} .0839888{col 32}{space 2} .0144968{col 43}{space 1}    5.79{col 52}{space 3}0.000{col 60}{space 4} .0555756{col 73}{space 3}  .112402
{txt}{space 16}2  {c |}{col 20}{res}{space 2} .0805279{col 32}{space 2} .0105905{col 43}{space 1}    7.60{col 52}{space 3}0.000{col 60}{space 4} .0597709{col 73}{space 3}  .101285
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. marginsplot, recast(bar) recastci(rcap) ///
>   ciopts(lw(medthick) color(black)) ///
>   plotopts(barw(.5) color(gs10)) ///
>   xmtick(0(1)2) ///
>   ylabel(0(.05).15) ///
>   xtitle(" ") ///
>   xlabel(0 "De-Escalation" 1 "No Change" 2 "Escalation") ///
>   ytitle(Predicted Probability of Negotiations) ///
>   title("Figure 2: Predicted Probability of Negotiations, Model 1", size(medlarge)) ///
>   graphregion(color(white)) ///
>   note("Note: 95% Confidence Intervals reported.", size(small)) ///
>   saving (Figure2.gph, replace)
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: battles_escalation{p_end}
{res}{txt}(file Figure2.gph saved)

{com}. 
.   
. *******************************************************************
. *******************************************************************
. *** Figure 3: Predicted Probabilities based on Model 2, Table 2 ***
. *******************************************************************
. *******************************************************************
. qui logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. margins, at(battles_escalation=(0 1 2) thirdparty_involvement=(0(1)1)) vsquish
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}     1,179
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(neg_DV), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:thirdparty_involvement}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0805623{col 26}{space 2} .0306683{col 37}{space 1}    2.63{col 46}{space 3}0.009{col 54}{space 4} .0204536{col 67}{space 3}  .140671
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1363088{col 26}{space 2} .0190498{col 37}{space 1}    7.16{col 46}{space 3}0.000{col 54}{space 4} .0989718{col 67}{space 3} .1736457
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0604265{col 26}{space 2} .0201262{col 37}{space 1}    3.00{col 46}{space 3}0.003{col 54}{space 4} .0209799{col 67}{space 3} .0998731
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1285112{col 26}{space 2} .0335462{col 37}{space 1}    3.83{col 46}{space 3}0.000{col 54}{space 4} .0627618{col 67}{space 3} .1942605
{txt}{space 10}5  {c |}{col 14}{res}{space 2}  .024692{col 26}{space 2} .0112195{col 37}{space 1}    2.20{col 46}{space 3}0.028{col 54}{space 4} .0027023{col 67}{space 3} .0466818
{txt}{space 10}6  {c |}{col 14}{res}{space 2} .1357478{col 26}{space 2} .0185702{col 37}{space 1}    7.31{col 46}{space 3}0.000{col 54}{space 4}  .099351{col 67}{space 3} .1721446
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. #delimit;
{txt}delimiter now ;
{com}. marginsplot, recast(bar) recastci(rcap) 
>   ci1opts(lw(medthick) color(black))
>   ci2opts(color(none) blcolor(none))
>   plot1opts(barw(.5) color(gs9)) 
>   plot2opts(barw(.5) blwidth(none)  color(none)) 
>   xmtick(0(1)2) 
>   ytitle("Predicted Probability of Negotiations" " ") 
>   xtitle(" ")
>   xlabel(0 "De-Escalation" 1 "No Change" 2 "Escalation", labsize(small)) 
>   title(No Third-Party Involvement, size(medlarge)) 
>   graphregion(color(white)) 
>   legend(off)
>   saving (figure3a.gph, replace) ;
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: battles_escalation thirdparty_involvement{p_end}
{res}{txt}(file figure3a.gph saved)

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. #delimit;
{txt}delimiter now ;
{com}. marginsplot, recast(bar) recastci(rcap) 
>   ci1opts(color(none) blcolor(none))
>   ci2opts(lw(medthick) color(black))
>   plot1opts( barw(.5) blwidth(none) color(none)) 
>   plot2opts(barw(.5) color(gs12)) 
>   xmtick(0(1)2) 
>   ytitle("Predicted Probability of Negotiations" " ") 
>   xtitle(" ")
>   xlabel(0 "De-Escalation" 1 "No Change" 2 "Escalation", labsize(small)) 
>   title(Third-Party Involvement, size(medlarge)) 
>   graphregion(color(white)) 
>   legend(off)
>   saving (figure3b.gph, replace) ;
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: battles_escalation thirdparty_involvement{p_end}
{res}{txt}(file figure3b.gph saved)

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. #delimit;
{txt}delimiter now ;
{com}. graph combine figure3a.gph figure3b.gph ,
>   col(2)
>   title("Figure 3: Predicted Probability of Negotiations, Model 2", size(medlarge))
>   subtitle("", size(medsmall))
>   graphregion(color(white))
>   note("Note: 95% Confidence Intervals reported.", size(small)) 
>   saving(Figure3.gph, replace)  ;
{res}{txt}(file Figure3.gph saved)

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
. *******************************************************
. *******************************************************
. *** Figure 4: Change in Probability of Negotiations ***
. *******************************************************
. *******************************************************
. qui logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. margins, dydx(battles_escalation) at(thirdparty_involvement=(0(1)1)) vsquish    
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,179
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(neg_DV), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.battles_escalation 2.battles_escalation}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:thirdparty_involvement}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:thirdparty_involvement}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.battles_~n{col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.battles_~n {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0201357{col 26}{space 2} .0315082{col 37}{space 1}   -0.64{col 46}{space 3}0.523{col 54}{space 4}-.0818908{col 67}{space 3} .0416193
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0077976{col 26}{space 2} .0403616{col 37}{space 1}   -0.19{col 46}{space 3}0.847{col 54}{space 4}-.0869049{col 67}{space 3} .0713096
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2.battles_~n {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0558703{col 26}{space 2} .0256516{col 37}{space 1}   -2.18{col 46}{space 3}0.029{col 54}{space 4}-.1061464{col 67}{space 3}-.0055941
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.000561{col 26}{space 2} .0236512{col 37}{space 1}   -0.02{col 46}{space 3}0.981{col 54}{space 4}-.0469165{col 67}{space 3} .0457945
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. #delimit;
{txt}delimiter now ;
{com}. marginsplot, recast(bar) recastci(rcap) yline(0, lw(thin)) 
>   ci1opts(lw(none) color(none)) 
>   ci2opts(lw(thin) color(black)) 
>   plot1opts(barw(.75) blwidth(none) color(none)) 
>   plot2opts(barw(.75) color(gs9)) 
>   xmtick(0(1)1) 
>   ylabel(-.1(.05)0.05) 
>   ytick(-.1(.05)0.05)
>   xlabel(0 "No 3rd Party" 1 "3rd Party Involvement") 
>   ytitle("Change in Probability of Negotiations" " ") 
>   xtitle(" ") 
>   title("Effect of Post-Negotiation (De-)Escalation", size(med) span) 
>   subtitle("Conditional on Third-Party Involvement", size(small) span) 
>   graphregion(color(white)) 
>   legend(off) 
>   saving (m2_fdgraph1.gph, replace) ;
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: thirdparty_involvement _deriv{p_end}
{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(file m2_fdgraph1.gph saved)

{com}.   #delimit cr
{txt}delimiter now cr
{com}. 
. qui logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. margins, dydx(thirdparty_involvement) at(battles_escalation=(0(1)2)) vsquish    
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     1,179
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(neg_DV), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:1.thirdparty_involvement}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:battles_escalation}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.thirdpar~t{col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.thirdpar~t {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0557465{col 26}{space 2} .0424686{col 37}{space 1}    1.31{col 46}{space 3}0.189{col 54}{space 4}-.0274904{col 67}{space 3} .1389834
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0680846{col 26}{space 2} .0455113{col 37}{space 1}    1.50{col 46}{space 3}0.135{col 54}{space 4}-.0211158{col 67}{space 3} .1572851
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .1110558{col 26}{space 2} .0215251{col 37}{space 1}    5.16{col 46}{space 3}0.000{col 54}{space 4} .0688673{col 67}{space 3} .1532442
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}. #delimit;
{txt}delimiter now ;
{com}. marginsplot, recast(bar) recastci(rcap) yline(0, lw(thin)) 
>   ci1opts(lw(thin) color(black)) 
>   plot1opts(barw(.75) color(gs10)) 
>   xmtick(0(1)1) 
>   ylabel(-.05(.05)0.15) 
>   ytick(-.05(.05)0.15)
>   xlabel(0 "De-Escalation" 1 "No Change" 2 "Escalation") 
>   ytitle("Change in Probability of Negotiations" " ") 
>   xtitle(" ") 
>   title("Effect of Third-Party Involvement", size(med) span) 
>   subtitle("Conditional on Post-Negotiation (De-)Escalation", size(small) span) 
>   graphregion(color(white)) 
>   legend(off) 
>   saving (m2_fdgraph2.gph, replace) ;
{res}
{text}{p 2 6 2}Variables that uniquely identify margins: battles_escalation{p_end}
{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(file m2_fdgraph2.gph saved)

{com}.   #delimit cr
{txt}delimiter now cr
{com}. 
. #delimit;
{txt}delimiter now ;
{com}. graph combine m2_fdgraph1.gph m2_fdgraph2.gph ,
>   col(2)
>   title("Figure 4: Change in  Probability of Negotiations", size(med))
>   subtitle("", size(medsmall))
>   graphregion(color(white))
>   note("Note: 95% Confidence Intervals reported.", size(small))
>   saving(Figure4.gph, replace)  ;
{res}{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(file Figure4.gph saved)

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
. 
. *** Footnote 27: Alternative Binning Algorithms
. logit neg_DV de_esc_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t c.t##c.t##c.t, cl(dyadid)

{txt}note: t omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-366.37411}  
Iteration 1:{space 3}log pseudolikelihood = {res: -333.8543}  
Iteration 2:{space 3}log pseudolikelihood = {res:-328.59579}  
Iteration 3:{space 3}log pseudolikelihood = {res:-327.55408}  
Iteration 4:{space 3}log pseudolikelihood = {res:-326.84298}  
Iteration 5:{space 3}log pseudolikelihood = {res:-326.72387}  
Iteration 6:{space 3}log pseudolikelihood = {res:-326.72104}  
Iteration 7:{space 3}log pseudolikelihood = {res:-326.72104}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,045
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     79.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-326.72104{txt}{col 49}Pseudo R2{col 67}= {res}    0.1082

{txt}{ralign 87:(Std. Err. adjusted for {res:37} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}de_esc_matching {c |}{col 23}{res}{space 2}-.4827329{col 35}{space 2} .1659201{col 46}{space 1}   -2.91{col 55}{space 3}0.004{col 63}{space 4}-.8079303{col 76}{space 3}-.1575356
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .5459616{col 35}{space 2} .2082248{col 46}{space 1}    2.62{col 55}{space 3}0.009{col 63}{space 4} .1378485{col 76}{space 3} .9540746
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0334242{col 35}{space 2} .1745201{col 46}{space 1}   -0.19{col 55}{space 3}0.848{col 63}{space 4}-.3754773{col 76}{space 3} .3086288
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .6157796{col 35}{space 2} .1905647{col 46}{space 1}    3.23{col 55}{space 3}0.001{col 63}{space 4} .2422796{col 76}{space 3} .9892796
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .1128633{col 35}{space 2} .1810463{col 46}{space 1}    0.62{col 55}{space 3}0.533{col 63}{space 4} -.241981{col 76}{space 3} .4677076
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0015083{col 35}{space 2}  .002776{col 46}{space 1}   -0.54{col 55}{space 3}0.587{col 63}{space 4}-.0069492{col 76}{space 3} .0039325
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2060537{col 35}{space 2} .0633797{col 46}{space 1}   -3.25{col 55}{space 3}0.001{col 63}{space 4}-.3302757{col 76}{space 3}-.0818317
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0075496{col 35}{space 2} .0031412{col 46}{space 1}    2.40{col 55}{space 3}0.016{col 63}{space 4} .0013929{col 76}{space 3} .0137063
{txt}{space 21} {c |}
{space 20}t {c |}{col 23}{res}{space 2}        0{col 35}{txt}  (omitted)
{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000889{col 35}{space 2} .0000395{col 46}{space 1}   -2.25{col 55}{space 3}0.024{col 63}{space 4}-.0001663{col 76}{space 3}-.0000116
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.9216433{col 35}{space 2} .5657886{col 46}{space 1}   -1.63{col 55}{space 3}0.103{col 63}{space 4}-2.030568{col 76}{space 3} .1872819
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 16 failures and 0 successes completely determined.{p_end}

{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(de_esc_matching) autocuts(ss)
{res}  .1380130874
  -7.897977394
  -9.748297971
  -10.48945733
  -10.19900853
  -9.591975676
  -9.405059347
  -9.629238548
  -9.437504131
  -9.313100668
  -9.394624412
  -9.372562628
  -9.466160253
  -9.505325628
  -9.32788684
  -9.611449864
  -9.311509256
  -9.29403794
  -9.256742888
  -9.137894111
  -9.264043512
  -9.052590149
  -9.232082353
  -9.069089167
  -9.405232335
  -9.178945683
  -8.981073876
  -9.28384465
  -9.239539956
  -8.933425871
  -9.332761904
  -9.250780785
  -9.116537174
  -8.845225181
  -8.855035268
  -9.150340406
  -9.168977425
  -9.034346723
  -9.073557152
  -8.877642276
  -8.903914143
  -8.992389262
  -8.989690215
  -9.09374831
  -8.923008791
  -8.764035095
  -8.810189814
  -8.619695618
  -8.630606714
  -9.015359905
  -8.956900071
  -9.064140698
  -8.976597488
  -8.69527618
  -9.241738023
  -8.734773802
  -8.824867543
  -8.745290502
  -8.660637164
  -8.337755304
  -8.218685426
  -8.545652549
  -8.640661738
  -8.694942247
  -8.726743849
  -8.44107644
  -8.837813708
  -8.493106344
  -8.693396213
  -8.350744549
  -8.877311459
  -8.38976469
  -8.634026821
  -8.569947104
  -8.102446956
  -8.269602471
  -8.086487377
  -8.101158244
  -7.943567179
  -7.690556217
  -7.523336344
  -7.596447785
  -7.819074036
  -7.854050405
  -7.822735455
  -8.022077575
  -8.227642276
  -8.139709108
  -8.147036609
  -7.970394606
  -8.066093864
  -8.228206985
  -8.163887343
  -8.371483961
  -8.390260601
  -8.153459416
  -8.184513527
  -8.426513616
  -8.221717472
  .8876607503
  -116.9097672
  -176.2627272
  -219.3166118
  -251.2661494
  -285.4666808
  -300.0298195
  -327.6089914
  -320.4740142
  -331.1662876
  -336.116175
  -369.1093634
  -360.6791533
  -348.6113599
  -370.0361781
  -398.0455415
  -370.2258789
  -385.1775959
  -407.2633705
  -369.5071527
  -386.7801953
  -399.256302
  -413.0599103
  -433.7203741
  -380.6545903
  -396.4890081
  -409.8453254
  -413.2897378
  -429.4313955
  -436.1323414
  -451.2289743
  -467.4652195
  -383.1833738
  -397.2630085
  -407.9846226
  -419.7213306
  -426.5815536
  -435.9947419
  -434.2369417
  -446.9666702
  -456.3946507
  -457.0431957
  -468.3392595
  -475.6506826
  -480.7916676
  -490.2123868
  -501.8213888
  -514.2031566
  -384.970751
  -394.88017
  -404.5475358
  -413.1822232
  -419.9464102
  -427.5803309
  -436.8405801
  -440.1253663
  -448.2544037
  -457.3518971
  -461.7947836
  -468.7683707
  -474.4655083
  -482.243672
  -488.507842
  -497.5558641
  -478.628933
  -486.8674866
  -495.4274773
  -500.4522078
  -508.2909518
  -508.8219425
  -515.4812818
  -522.6377936
  -515.1668368
  -523.3413667
  -528.4514486
  -533.5682074
  -538.8447093
  -540.209979
  -548.112755
  -553.5763312
  -552.2578619
  -559.6314841
  -564.4139743
  -562.8638234
  -564.1199552
  -570.4704056
  -574.6140929
  -580.3363414
  -584.0393312
  -587.5530237
  -590.6200166
  -596.451076
  -604.0197477
  -610.0710727
  -617.4752907
  -624.6262887
  -373.8146477
  -378.837804
  -383.8607136

{txt}Matching Summary:
-----------------
Number of strata: {res}637
{txt}Number of matched strata: {res}108

           {txt}  0    1
      All  {res}434  611
{txt}  Matched  {res}238  202
{txt}Unmatched  {res}196  409


{txt}Multivariate L1 distance: {res}.44069228

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 3.5e-16   1.4e-16         0         0         0         0         0
{txt}         str_centcomm  {res} 5.6e-17   1.8e-15         0         0         0         0         0
{txt}               parity  {res} 6.2e-17   1.4e-17         0         0         0         0         0
{txt}             groupnum  {res} 4.3e-16  -4.4e-16         0         0         0         0         0
{txt}          dyad_months  {res}   .1651    -3.837        -1        -5         0         1       -33
{txt}                    t  {res} 3.0e-16    .04739         0         1         0         0         0
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(de_esc_matching)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}169
{txt}Number of matched strata: {res}38

           {txt}  0    1
      All  {res}434  611
{txt}  Matched  {res}237  202
{txt}Unmatched  {res}197  409


{txt}Multivariate L1 distance: {res}.48740673

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res}6.0e-16  4.2e-16        0        0        0        0        0
{txt}         str_centcomm  {res}7.5e-16  3.1e-15        0        0        0        0        0
{txt}               parity  {res}2.2e-16  3.3e-16        0        0        0        0        0
{txt}             groupnum  {res}9.4e-16  1.1e-15        0        0        0        0        0
{txt}          dyad_months  {res} .16715   1.7806       -1       -4        4        0        5
{txt}                    t  {res} .19463   .73252        0        1        2        1        4
{txt}
{com}. logit neg_DV deesc_no_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t c.t##c.t##c.t, cl(dyadid)

{txt}note: t omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -293.986}  
Iteration 1:{space 3}log pseudolikelihood = {res:-261.28967}  
Iteration 2:{space 3}log pseudolikelihood = {res:-253.19774}  
Iteration 3:{space 3}log pseudolikelihood = {res:-252.28801}  
Iteration 4:{space 3}log pseudolikelihood = {res:-252.02521}  
Iteration 5:{space 3}log pseudolikelihood = {res:-251.75788}  
Iteration 6:{space 3}log pseudolikelihood = {res:-251.40602}  
Iteration 7:{space 3}log pseudolikelihood = {res:-251.22566}  
Iteration 8:{space 3}log pseudolikelihood = {res:-251.20318}  
Iteration 9:{space 3}log pseudolikelihood = {res:-251.20297}  
Iteration 10:{space 2}log pseudolikelihood = {res:-251.20297}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       873
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     72.74
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-251.20297{txt}{col 49}Pseudo R2{col 67}= {res}    0.1455

{txt}{ralign 87:(Std. Err. adjusted for {res:38} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}deesc_no_matching {c |}{col 23}{res}{space 2}-.3805752{col 35}{space 2} .2239486{col 46}{space 1}   -1.70{col 55}{space 3}0.089{col 63}{space 4}-.8195063{col 76}{space 3} .0583559
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4212313{col 35}{space 2} .2457755{col 46}{space 1}    1.71{col 55}{space 3}0.087{col 63}{space 4}-.0604798{col 76}{space 3} .9029425
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}  .074066{col 35}{space 2} .1168739{col 46}{space 1}    0.63{col 55}{space 3}0.526{col 63}{space 4}-.1550026{col 76}{space 3} .3031347
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .3463917{col 35}{space 2} .3250841{col 46}{space 1}    1.07{col 55}{space 3}0.287{col 63}{space 4}-.2907615{col 76}{space 3} .9835449
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}-.1522897{col 35}{space 2} .1629188{col 46}{space 1}   -0.93{col 55}{space 3}0.350{col 63}{space 4}-.4716046{col 76}{space 3} .1670253
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0006037{col 35}{space 2} .0023826{col 46}{space 1}   -0.25{col 55}{space 3}0.800{col 63}{space 4}-.0052735{col 76}{space 3} .0040661
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1523241{col 35}{space 2}  .056089{col 46}{space 1}   -2.72{col 55}{space 3}0.007{col 63}{space 4}-.2622565{col 76}{space 3}-.0423917
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0036651{col 35}{space 2} .0023508{col 46}{space 1}    1.56{col 55}{space 3}0.119{col 63}{space 4}-.0009423{col 76}{space 3} .0082725
{txt}{space 21} {c |}
{space 20}t {c |}{col 23}{res}{space 2}        0{col 35}{txt}  (omitted)
{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000324{col 35}{space 2} .0000243{col 46}{space 1}   -1.33{col 55}{space 3}0.183{col 63}{space 4}-.0000801{col 76}{space 3} .0000153
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.7942771{col 35}{space 2} .5458298{col 46}{space 1}   -1.46{col 55}{space 3}0.146{col 63}{space 4}-1.864084{col 76}{space 3} .2755297
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 94 failures and 0 successes completely determined.{p_end}

{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(deesc_no_matching) autocuts(ss)
{res}  .1152752991
  -3.956077732
  -5.727234671
  -5.351477295
  -5.32266508
  -5.751052063
  -5.47406589
  -5.201665675
  -4.996548204
  -5.046519929
  -5.155192616
  -5.429627426
  -5.477473218
  -5.341137645
  -5.127494657
  -5.219727014
  -5.279875424
  -5.32473322
  -5.39890277
  -5.037834622
  -5.074363647
  -5.1596903
  -5.296847115
  -5.140238394
  -5.365702955
  -5.085983618
  -4.842042386
  -5.24408657
  -5.240791264
  -5.062028775
  -5.278987802
  -5.174722387
  -5.087534076
  -5.090991979
  -4.963066202
  -5.25308642
  -5.225625387
  -5.199484434
  -5.027600245
  -4.954471545
  -5.027224407
  -5.094753375
  -5.207417758
  -5.308528473
  -5.065368204
  -5.037378545
  -4.975221605
  -4.685212726
  -4.807296704
  -4.993678366
  -5.028757911
  -4.925237826
  -5.079321608
  -4.675598495
  -5.312696267
  -4.772201921
  -4.833652808
  -4.749815951
  -4.660496006
  -4.449168264
  -4.548980408
  -4.711375717
  -4.825413612
  -4.88472057
  -4.806314922
  -4.599752232
  -4.875552833
  -4.524559378
  -4.741005134
  -4.397852752
  -5.215708289
  -4.592221708
  -4.756624536
  -4.742927044
  -4.307989512
  -4.483905083
  -4.26403321
  -4.267650344
  -4.125600009
  -3.995925045
  -3.933621065
  -4.04570289
  -4.202685818
  -4.268607436
  -4.19482101
  -4.243969306
  -4.35653229
  -4.56714678
  -4.522254677
  -4.186263321
  -4.30076289
  -4.650307357
  -4.086870832
  -4.505820863
  -4.457029546
  -4.380943332
  -4.455003806
  -4.425493814
  -4.471110737
  .169283409
  -16.14943338
  -26.99947422
  -35.26486932
  -42.01068699
  -48.35139573
  -52.72140968
  -57.16023199
  -60.15266352
  -64.31277634
  -68.39543877
  -71.72582041
  -75.69314395
  -79.02992758
  -82.0106158
  -83.44228203
  -83.77123786
  -88.91236273
  -88.19044384
  -92.77812128
  -90.85520616
  -95.26272946
  -92.95522823
  -96.78994071
  -101.0541115
  -97.06305381
  -101.0613467
  -104.0514728
  -98.0667263
  -101.8065406
  -105.3371648
  -107.5753234
  -110.965304
  -101.7534148
  -104.9650541
  -108.0180004
  -109.5949415
  -112.7800238
  -115.4216374
  -118.3733845
  -105.1987554
  -107.9190491
  -110.7576523
  -112.7537894
  -115.5573739
  -116.4410202
  -118.2225366
  -120.523583
  -123.3181676
  -125.8229862
  -108.3058631
  -110.6888226
  -113.0007578
  -115.275268
  -117.1382739
  -118.5736264
  -120.7478302
  -119.8238215
  -122.0916445
  -124.075126
  -125.4174112
  -127.6107552
  -128.7776634
  -130.583216
  -132.7991204
  -134.8074111
  -136.988351
  -110.9321807
  -112.7954249
  -114.5201402
  -116.3381252
  -118.0444293
  -119.4298306
  -120.7290391
  -122.4850468
  -124.2045184
  -124.7012603
  -126.4972489
  -127.453343
  -129.1695358
  -130.9465168
  -127.9688358
  -129.563584
  -131.0633368
  -132.0015965
  -133.6172692
  -132.7525518
  -134.4285825
  -135.8807242
  -137.3886265
  -138.1506216
  -139.8351331
  -140.8074225
  -141.6626042
  -142.8494164
  -144.1622259
  -145.8763622
  -147.4181655
  -148.8794617

{txt}Matching Summary:
-----------------
Number of strata: {res}354
{txt}Number of matched strata: {res}28

           {txt}  0    1
      All  {res}434  439
{txt}  Matched  {res}117   78
{txt}Unmatched  {res}317  361


{txt}Multivariate L1 distance: {res}.38162393

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res}       0         0         0         0         0         0         0
{txt}         str_centcomm  {res} 1.1e-16  -4.4e-16         0         0         0         0         0
{txt}               parity  {res}       0         0         0         0         0         0         0
{txt}             groupnum  {res}       0  -2.2e-16         0         0         0         0         0
{txt}          dyad_months  {res}  .18782   -.48348        -1         3         2         7        -2
{txt}                    t  {res}  .01923    .13583         0         0         0         0        -1
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(deesc_no_matching)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}113
{txt}Number of matched strata: {res}24

           {txt}  0    1
      All  {res}434  439
{txt}  Matched  {res}180  119
{txt}Unmatched  {res}254  320


{txt}Multivariate L1 distance: {res}.67689076

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res}1.8e-16  2.8e-17        0        0        0        0        0
{txt}         str_centcomm  {res}3.6e-16        0        0        0        0        0        0
{txt}               parity  {res}2.8e-17  5.6e-17        0        0        0        0        0
{txt}             groupnum  {res}3.6e-16  2.2e-16        0        0        0        0        0
{txt}          dyad_months  {res} .19404  -.53557       -1       -4       -3        1        7
{txt}                    t  {res} .21535     2.47        0        1        3        4        0
{txt}
{com}. logit neg_DV no_esc_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t c.t##c.t##c.t, cl(dyadid)

{txt}note: t omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-270.18457}  
Iteration 1:{space 3}log pseudolikelihood = {res:-240.79882}  
Iteration 2:{space 3}log pseudolikelihood = {res:-232.62519}  
Iteration 3:{space 3}log pseudolikelihood = {res:-231.91243}  
Iteration 4:{space 3}log pseudolikelihood = {res:-231.38961}  
Iteration 5:{space 3}log pseudolikelihood = {res:-230.52476}  
Iteration 6:{space 3}log pseudolikelihood = {res:-229.56271}  
Iteration 7:{space 3}log pseudolikelihood = {res: -229.1365}  
Iteration 8:{space 3}log pseudolikelihood = {res:-229.07613}  
Iteration 9:{space 3}log pseudolikelihood = {res:-229.07592}  
Iteration 10:{space 2}log pseudolikelihood = {res:-229.07592}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,050
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     58.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-229.07592{txt}{col 49}Pseudo R2{col 67}= {res}    0.1522

{txt}{ralign 87:(Std. Err. adjusted for {res:36} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}no_esc_matching {c |}{col 23}{res}{space 2} .0054806{col 35}{space 2} .2664415{col 46}{space 1}    0.02{col 55}{space 3}0.984{col 63}{space 4}-.5167351{col 76}{space 3} .5276964
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4280526{col 35}{space 2} .2145386{col 46}{space 1}    2.00{col 55}{space 3}0.046{col 63}{space 4} .0075647{col 76}{space 3} .8485404
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0229677{col 35}{space 2} .1295048{col 46}{space 1}   -0.18{col 55}{space 3}0.859{col 63}{space 4}-.2767924{col 76}{space 3} .2308571
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5086011{col 35}{space 2} .2403144{col 46}{space 1}    2.12{col 55}{space 3}0.034{col 63}{space 4} .0375935{col 76}{space 3} .9796087
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .1698189{col 35}{space 2} .1724519{col 46}{space 1}    0.98{col 55}{space 3}0.325{col 63}{space 4}-.1681805{col 76}{space 3} .5078184
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0017975{col 35}{space 2} .0037453{col 46}{space 1}   -0.48{col 55}{space 3}0.631{col 63}{space 4}-.0091382{col 76}{space 3} .0055431
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2226182{col 35}{space 2} .0583231{col 46}{space 1}   -3.82{col 55}{space 3}0.000{col 63}{space 4}-.3369293{col 76}{space 3} -.108307
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0065668{col 35}{space 2} .0025692{col 46}{space 1}    2.56{col 55}{space 3}0.011{col 63}{space 4} .0015312{col 76}{space 3} .0116024
{txt}{space 21} {c |}
{space 20}t {c |}{col 23}{res}{space 2}        0{col 35}{txt}  (omitted)
{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2} -.000062{col 35}{space 2} .0000305{col 46}{space 1}   -2.04{col 55}{space 3}0.042{col 63}{space 4}-.0001217{col 76}{space 3}-2.35e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-1.276446{col 35}{space 2} .6601454{col 46}{space 1}   -1.93{col 55}{space 3}0.053{col 63}{space 4}-2.570307{col 76}{space 3} .0174157
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 110 failures and 0 successes completely determined.{p_end}

{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(no_esc_matching) autocuts(ss)
{res}  .1385418732
  -4.469958358
  -5.107541807
  -4.511038403
  -4.834622249
  -4.69627867
  -4.670235971
  -4.646638905
  -4.380549497
  -4.307224536
  -4.609485696
  -4.480269681
  -4.672183326
  -4.686694428
  -4.563421244
  -4.950046269
  -5.06918151
  -4.973439531
  -4.991146317
  -4.836010311
  -4.921607509
  -4.838915629
  -4.775935649
  -4.72602287
  -4.823121158
  -4.711000778
  -4.66224543
  -4.724072972
  -4.745772563
  -4.566296517
  -4.818944177
  -4.839356534
  -4.920856393
  -4.738669015
  -4.59369423
  -4.779584463
  -4.655156644
  -4.955947274
  -4.546642465
  -4.546070461
  -4.42720893
  -4.568874347
  -4.633903469
  -4.602103726
  -4.543040959
  -4.383638391
  -4.564928846
  -4.254990416
  -4.250823867
  -4.505353956
  -4.631098047
  -4.31810532
  -4.588104565
  -4.457205073
  -4.490593021
  -4.161312711
  -4.501466337
  -4.275721895
  -4.048336846
  -3.870711878
  -3.887709957
  -4.09715799
  -4.187278296
  -4.255837299
  -3.968516908
  -4.111132142
  -4.288058024
  -4.138502992
  -3.951552877
  -3.890475246
  -4.239629329
  -4.123912133
  -4.076905734
  -3.841624194
  -3.73494613
  -3.925392502
  -4.357939899
  -3.556419917
  -3.517596769
  -3.366299822
  -3.282946904
  -3.248471277
  -3.406539901
  -3.432811157
  -3.592737749
  -3.61227859
  -3.463410076
  -3.640614954
  -3.750676764
  -3.815475797
  -3.645113562
  -3.474868306
  -3.699579317
  -3.826651084
  -4.12586493
  -3.432571551
  -3.536249423
  -3.614472678
  -3.921026646
  .2036448349
  -25.88445485
  -38.51637587
  -48.67629037
  -54.47206193
  -59.70089463
  -64.5981495
  -70.64475297
  -73.85986341
  -78.3480332
  -81.8209085
  -83.43738221
  -86.81140976
  -88.85161182
  -90.64080824
  -90.9370723
  -89.88954155
  -95.1152917
  -93.90605782
  -98.45118906
  -95.56647935
  -99.61480281
  -95.94191222
  -99.11277977
  -103.3255609
  -97.35776632
  -101.2371012
  -102.9627275
  -95.74592427
  -99.54621644
  -102.7445586
  -104.15625
  -106.9098969
  -97.15182506
  -100.3805244
  -102.5035557
  -103.2947591
  -106.3500902
  -108.0150403
  -110.7599196
  -98.76689025
  -101.4463836
  -103.7228151
  -105.1527386
  -107.6980034
  -107.1863581
  -108.1622843
  -109.8100723
  -112.0374438
  -114.394526
  -99.33537267
  -101.6028561
  -103.7453258
  -105.638801
  -106.8653829
  -107.8219932
  -109.3777794
  -107.8983085
  -110.1175611
  -111.2740667
  -111.4270486
  -113.4283714
  -114.0706403
  -115.214867
  -116.6398961
  -118.5225914
  -120.4191752
  -100.4363314
  -102.2265222
  -103.897547
  -105.5562825
  -106.656835
  -107.8121836
  -108.6941533
  -110.0929655
  -111.7609608
  -111.1348694
  -112.7160425
  -113.3176082
  -114.8096475
  -116.5059065
  -113.721199
  -115.1646941
  -116.226577
  -116.8602107
  -118.0645549
  -116.6697711
  -118.2417822
  -119.1570621
  -120.2461601
  -119.8141577
  -121.1067562
  -121.7311794
  -122.0812756
  -122.8572338
  -123.5129616
  -124.6778381
  -125.7482106
  -127.1485512

{txt}Matching Summary:
-----------------
Number of strata: {res}404
{txt}Number of matched strata: {res}57

           {txt}  0    1
      All  {res}439  611
{txt}  Matched  {res}130  173
{txt}Unmatched  {res}309  438


{txt}Multivariate L1 distance: {res}.6716763

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res}1.5e-16  8.3e-17        0        0        0        0        0
{txt}         str_centcomm  {res}8.7e-17  4.4e-16        0        0        0        0        0
{txt}               parity  {res}1.1e-16        0        0        0        0        0        0
{txt}             groupnum  {res}2.1e-16  1.8e-15        0        0        0        0        0
{txt}          dyad_months  {res} .31753  -4.2422        0       -8       -3       -5        7
{txt}                    t  {res}5.9e-17   .16532        0        1        0        0        0
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(no_esc_matching)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}145
{txt}Number of matched strata: {res}20

           {txt}  0    1
      All  {res}439  611
{txt}  Matched  {res} 98  176
{txt}Unmatched  {res}341  435


{txt}Multivariate L1 distance: {res}.56899559

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 5.6e-17         0         0         0         0         0         0
{txt}         str_centcomm  {res} 2.0e-16   1.8e-15         0         0         0         0         0
{txt}               parity  {res} 6.9e-17   2.8e-17         0         0         0         0         0
{txt}             groupnum  {res} 2.8e-17  -1.8e-15         0         0         0         0         0
{txt}          dyad_months  {res}  .36312    .50964        -3        -1         8        -7       -12
{txt}                    t  {res}  .19415    .31851         0         2        -4        -3         5
{txt}
{com}. 
. 
. 
. **********************************
. **********************************
. *** Table 3: Matching Analyses ***
. **********************************
. **********************************
. * Model 1, Table 3 (matching: control=de-escalation; treatment=escalation)
. logit neg_DV de_esc_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-366.37411}  
Iteration 1:{space 3}log pseudolikelihood = {res: -333.8543}  
Iteration 2:{space 3}log pseudolikelihood = {res:-328.59579}  
Iteration 3:{space 3}log pseudolikelihood = {res:-327.55408}  
Iteration 4:{space 3}log pseudolikelihood = {res:-326.84298}  
Iteration 5:{space 3}log pseudolikelihood = {res:-326.72387}  
Iteration 6:{space 3}log pseudolikelihood = {res:-326.72104}  
Iteration 7:{space 3}log pseudolikelihood = {res:-326.72104}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,045
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     79.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-326.72104{txt}{col 49}Pseudo R2{col 67}= {res}    0.1082

{txt}{ralign 87:(Std. Err. adjusted for {res:37} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}de_esc_matching {c |}{col 23}{res}{space 2}-.4827329{col 35}{space 2} .1659201{col 46}{space 1}   -2.91{col 55}{space 3}0.004{col 63}{space 4}-.8079303{col 76}{space 3}-.1575356
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .5459616{col 35}{space 2} .2082248{col 46}{space 1}    2.62{col 55}{space 3}0.009{col 63}{space 4} .1378485{col 76}{space 3} .9540746
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0334242{col 35}{space 2} .1745201{col 46}{space 1}   -0.19{col 55}{space 3}0.848{col 63}{space 4}-.3754773{col 76}{space 3} .3086288
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .6157796{col 35}{space 2} .1905647{col 46}{space 1}    3.23{col 55}{space 3}0.001{col 63}{space 4} .2422796{col 76}{space 3} .9892796
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .1128633{col 35}{space 2} .1810463{col 46}{space 1}    0.62{col 55}{space 3}0.533{col 63}{space 4} -.241981{col 76}{space 3} .4677076
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0015083{col 35}{space 2}  .002776{col 46}{space 1}   -0.54{col 55}{space 3}0.587{col 63}{space 4}-.0069492{col 76}{space 3} .0039325
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2060537{col 35}{space 2} .0633797{col 46}{space 1}   -3.25{col 55}{space 3}0.001{col 63}{space 4}-.3302757{col 76}{space 3}-.0818317
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0075496{col 35}{space 2} .0031412{col 46}{space 1}    2.40{col 55}{space 3}0.016{col 63}{space 4} .0013929{col 76}{space 3} .0137063
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000889{col 35}{space 2} .0000395{col 46}{space 1}   -2.25{col 55}{space 3}0.024{col 63}{space 4}-.0001663{col 76}{space 3}-.0000116
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.9216433{col 35}{space 2} .5657886{col 46}{space 1}   -1.63{col 55}{space 3}0.103{col 63}{space 4}-2.030568{col 76}{space 3} .1872819
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 16 failures and 0 successes completely determined.{p_end}

{com}. imb weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(de_esc_matching)
{res}
{txt}Multivariate L1 distance: {res}.770611

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res} .00049  -.00049        0        0        0        0        0
{txt}         str_centcomm  {res} .10166   .24498        1        0        0        1        0
{txt}               parity  {res} .06745   .06745        0        0        0        0        0
{txt}             groupnum  {res} .15251  -.17109        0        0       -1        0       -1
{txt}          dyad_months  {res} .19398   19.735       -1       10       29       22       61
{txt}                    t  {res} .21151   9.2001        0        2        6       17       22
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(de_esc_matching) autocuts(fd)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}295
{txt}Number of matched strata: {res}42

           {txt}  0    1
      All  {res}434  611
{txt}  Matched  {res}167  151
{txt}Unmatched  {res}267  460


{txt}Multivariate L1 distance: {res}.35737221

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 2.2e-16  -2.2e-16         0         0         0         0         0
{txt}         str_centcomm  {res} 4.5e-16  -8.9e-16         0         0         0         0         0
{txt}               parity  {res} 9.0e-17  -1.8e-16         0         0         0         0         0
{txt}             groupnum  {res} 3.7e-16         0         0         0         0         0         0
{txt}          dyad_months  {res}  .14594    .91436        -1         3         2        -3        -8
{txt}                    t  {res}  .01865    .16964         0         1         0         0        -2
{txt}
{com}. logit neg_DV de_esc_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-161.96791}  
Iteration 1:{space 3}log pseudolikelihood = {res:-148.99441}  
Iteration 2:{space 3}log pseudolikelihood = {res:-147.01072}  
Iteration 3:{space 3}log pseudolikelihood = {res:-146.36645}  
Iteration 4:{space 3}log pseudolikelihood = {res:-146.28941}  
Iteration 5:{space 3}log pseudolikelihood = {res:-146.28861}  
Iteration 6:{space 3}log pseudolikelihood = {res:-146.28861}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       318
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     36.15
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-146.28861{txt}{col 49}Pseudo R2{col 67}= {res}    0.0968

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in dyadid)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         neg_DV{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching {c |}{col 17}{res}{space 2} -1.06721{col 29}{space 2} .2962734{col 40}{space 1}   -3.60{col 49}{space 3}0.000{col 57}{space 4}-1.647895{col 70}{space 3}-.4865249
{txt}{space 4}dyad_months {c |}{col 17}{res}{space 2} -.001084{col 29}{space 2}  .003887{col 40}{space 1}   -0.28{col 49}{space 3}0.780{col 57}{space 4}-.0087023{col 70}{space 3} .0065343
{txt}{space 14}t {c |}{col 17}{res}{space 2}-.8935256{col 29}{space 2} .4743798{col 40}{space 1}   -1.88{col 49}{space 3}0.060{col 57}{space 4}-1.823293{col 70}{space 3} .0362417
{txt}{space 15} {c |}
{space 8}c.t#c.t {c |}{col 17}{res}{space 2} .0964035{col 29}{space 2} .0539791{col 40}{space 1}    1.79{col 49}{space 3}0.074{col 57}{space 4}-.0093935{col 70}{space 3} .2022006
{txt}{space 15} {c |}
{space 4}c.t#c.t#c.t {c |}{col 17}{res}{space 2}-.0030872{col 29}{space 2} .0016894{col 40}{space 1}   -1.83{col 49}{space 3}0.068{col 57}{space 4}-.0063984{col 70}{space 3}  .000224
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 1.159837{col 29}{space 2} .8130422{col 40}{space 1}    1.43{col 49}{space 3}0.154{col 57}{space 4}-.4336963{col 70}{space 3} 2.753371
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 1 failure and 0 successes completely determined.{p_end}

{com}. estimates store m1t3
{txt}
{com}.         
. * Model 2, Table 3 (matching: control=de-escalation; treatment=no change)
. logit neg_DV deesc_no_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -293.986}  
Iteration 1:{space 3}log pseudolikelihood = {res:-261.28967}  
Iteration 2:{space 3}log pseudolikelihood = {res:-253.19774}  
Iteration 3:{space 3}log pseudolikelihood = {res:-252.28801}  
Iteration 4:{space 3}log pseudolikelihood = {res:-252.02521}  
Iteration 5:{space 3}log pseudolikelihood = {res:-251.75788}  
Iteration 6:{space 3}log pseudolikelihood = {res:-251.40602}  
Iteration 7:{space 3}log pseudolikelihood = {res:-251.22566}  
Iteration 8:{space 3}log pseudolikelihood = {res:-251.20318}  
Iteration 9:{space 3}log pseudolikelihood = {res:-251.20297}  
Iteration 10:{space 2}log pseudolikelihood = {res:-251.20297}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       873
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     72.74
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-251.20297{txt}{col 49}Pseudo R2{col 67}= {res}    0.1455

{txt}{ralign 87:(Std. Err. adjusted for {res:38} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}deesc_no_matching {c |}{col 23}{res}{space 2}-.3805752{col 35}{space 2} .2239486{col 46}{space 1}   -1.70{col 55}{space 3}0.089{col 63}{space 4}-.8195063{col 76}{space 3} .0583559
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4212313{col 35}{space 2} .2457755{col 46}{space 1}    1.71{col 55}{space 3}0.087{col 63}{space 4}-.0604798{col 76}{space 3} .9029425
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}  .074066{col 35}{space 2} .1168739{col 46}{space 1}    0.63{col 55}{space 3}0.526{col 63}{space 4}-.1550026{col 76}{space 3} .3031347
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .3463917{col 35}{space 2} .3250841{col 46}{space 1}    1.07{col 55}{space 3}0.287{col 63}{space 4}-.2907615{col 76}{space 3} .9835449
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}-.1522897{col 35}{space 2} .1629188{col 46}{space 1}   -0.93{col 55}{space 3}0.350{col 63}{space 4}-.4716046{col 76}{space 3} .1670253
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0006037{col 35}{space 2} .0023826{col 46}{space 1}   -0.25{col 55}{space 3}0.800{col 63}{space 4}-.0052735{col 76}{space 3} .0040661
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1523241{col 35}{space 2}  .056089{col 46}{space 1}   -2.72{col 55}{space 3}0.007{col 63}{space 4}-.2622565{col 76}{space 3}-.0423917
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0036651{col 35}{space 2} .0023508{col 46}{space 1}    1.56{col 55}{space 3}0.119{col 63}{space 4}-.0009423{col 76}{space 3} .0082725
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000324{col 35}{space 2} .0000243{col 46}{space 1}   -1.33{col 55}{space 3}0.183{col 63}{space 4}-.0000801{col 76}{space 3} .0000153
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.7942771{col 35}{space 2} .5458298{col 46}{space 1}   -1.46{col 55}{space 3}0.146{col 63}{space 4}-1.864084{col 76}{space 3} .2755297
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 94 failures and 0 successes completely determined.{p_end}

{com}. imb weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(deesc_no_matching)
{res}
{txt}Multivariate L1 distance: {res}.84216852

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res} .02284  -.02284        0        0        0        0        0
{txt}         str_centcomm  {res} .15692   -.2109        0       -1        0        0        0
{txt}               parity  {res} .00974  -.00974        0        0        0        0        0
{txt}             groupnum  {res} .10371   .18352        0        0        0        0        0
{txt}          dyad_months  {res} .24825   33.721       -1       13       37       41       61
{txt}                    t  {res} .39332   45.733        0        5       26       82      128
{txt}
{com}. estimates store imb2_pre
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(deesc_no_matching) autocuts(fd) 
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}169
{txt}Number of matched strata: {res}23

           {txt}  0    1
      All  {res}434  439
{txt}  Matched  {res}140  103
{txt}Unmatched  {res}294  336


{txt}Multivariate L1 distance: {res}.50641915

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 1.9e-16   5.6e-17         0         0         0         0         0
{txt}         str_centcomm  {res} 1.4e-16  -8.9e-16         0         0         0         0         0
{txt}               parity  {res} 1.1e-16         0         0         0         0         0         0
{txt}             groupnum  {res} 8.7e-17  -2.2e-15         0         0         0         0         0
{txt}          dyad_months  {res}  .04854    4.8798        -1         3         9         1         7
{txt}                    t  {res}  .23194    .94287         0         1         2         1        -6
{txt}
{com}. logit neg_DV deesc_no_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-147.13265}  
Iteration 1:{space 3}log pseudolikelihood = {res:-125.67514}  
Iteration 2:{space 3}log pseudolikelihood = {res:-123.72171}  
Iteration 3:{space 3}log pseudolikelihood = {res: -122.2862}  
Iteration 4:{space 3}log pseudolikelihood = {res:-122.09569}  
Iteration 5:{space 3}log pseudolikelihood = {res:-122.09298}  
Iteration 6:{space 3}log pseudolikelihood = {res:-122.09298}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       243
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}      9.84
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0799
{txt}Log pseudolikelihood = {res}-122.09298{txt}{col 49}Pseudo R2{col 67}= {res}    0.1702

{txt}{ralign 83:(Std. Err. adjusted for {res:29} clusters in dyadid)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}           neg_DV{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
deesc_no_matching {c |}{col 19}{res}{space 2}-1.252057{col 31}{space 2} .5594911{col 42}{space 1}   -2.24{col 51}{space 3}0.025{col 59}{space 4}-2.348639{col 72}{space 3}-.1554745
{txt}{space 6}dyad_months {c |}{col 19}{res}{space 2} .0150984{col 31}{space 2} .0084354{col 42}{space 1}    1.79{col 51}{space 3}0.073{col 59}{space 4}-.0014347{col 72}{space 3} .0316314
{txt}{space 16}t {c |}{col 19}{res}{space 2}-1.748422{col 31}{space 2} .7317584{col 42}{space 1}   -2.39{col 51}{space 3}0.017{col 59}{space 4}-3.182642{col 72}{space 3} -.314202
{txt}{space 17} {c |}
{space 10}c.t#c.t {c |}{col 19}{res}{space 2} .2103999{col 31}{space 2}  .092322{col 42}{space 1}    2.28{col 51}{space 3}0.023{col 59}{space 4} .0294521{col 72}{space 3} .3913477
{txt}{space 17} {c |}
{space 6}c.t#c.t#c.t {c |}{col 19}{res}{space 2}-.0073115{col 31}{space 2} .0033546{col 42}{space 1}   -2.18{col 51}{space 3}0.029{col 59}{space 4}-.0138863{col 72}{space 3}-.0007367
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2} 2.236734{col 31}{space 2} 1.516766{col 42}{space 1}    1.47{col 51}{space 3}0.140{col 59}{space 4}-.7360717{col 72}{space 3} 5.209541
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 2 failures and 0 successes completely determined.{p_end}

{com}. estimates store m2t3
{txt}
{com}. 
. * Model 3, Table 3 (matching: control=no change; treatment=escalation)
. logit neg_DV no_esc_matching weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-270.18457}  
Iteration 1:{space 3}log pseudolikelihood = {res:-240.79882}  
Iteration 2:{space 3}log pseudolikelihood = {res:-232.62519}  
Iteration 3:{space 3}log pseudolikelihood = {res:-231.91243}  
Iteration 4:{space 3}log pseudolikelihood = {res:-231.38961}  
Iteration 5:{space 3}log pseudolikelihood = {res:-230.52476}  
Iteration 6:{space 3}log pseudolikelihood = {res:-229.56271}  
Iteration 7:{space 3}log pseudolikelihood = {res: -229.1365}  
Iteration 8:{space 3}log pseudolikelihood = {res:-229.07613}  
Iteration 9:{space 3}log pseudolikelihood = {res:-229.07592}  
Iteration 10:{space 2}log pseudolikelihood = {res:-229.07592}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,050
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     58.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-229.07592{txt}{col 49}Pseudo R2{col 67}= {res}    0.1522

{txt}{ralign 87:(Std. Err. adjusted for {res:36} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}no_esc_matching {c |}{col 23}{res}{space 2} .0054806{col 35}{space 2} .2664415{col 46}{space 1}    0.02{col 55}{space 3}0.984{col 63}{space 4}-.5167351{col 76}{space 3} .5276964
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4280526{col 35}{space 2} .2145386{col 46}{space 1}    2.00{col 55}{space 3}0.046{col 63}{space 4} .0075647{col 76}{space 3} .8485404
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0229677{col 35}{space 2} .1295048{col 46}{space 1}   -0.18{col 55}{space 3}0.859{col 63}{space 4}-.2767924{col 76}{space 3} .2308571
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5086011{col 35}{space 2} .2403144{col 46}{space 1}    2.12{col 55}{space 3}0.034{col 63}{space 4} .0375935{col 76}{space 3} .9796087
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .1698189{col 35}{space 2} .1724519{col 46}{space 1}    0.98{col 55}{space 3}0.325{col 63}{space 4}-.1681805{col 76}{space 3} .5078184
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0017975{col 35}{space 2} .0037453{col 46}{space 1}   -0.48{col 55}{space 3}0.631{col 63}{space 4}-.0091382{col 76}{space 3} .0055431
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2226182{col 35}{space 2} .0583231{col 46}{space 1}   -3.82{col 55}{space 3}0.000{col 63}{space 4}-.3369293{col 76}{space 3} -.108307
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0065668{col 35}{space 2} .0025692{col 46}{space 1}    2.56{col 55}{space 3}0.011{col 63}{space 4} .0015312{col 76}{space 3} .0116024
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2} -.000062{col 35}{space 2} .0000305{col 46}{space 1}   -2.04{col 55}{space 3}0.042{col 63}{space 4}-.0001217{col 76}{space 3}-2.35e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-1.276446{col 35}{space 2} .6601454{col 46}{space 1}   -1.93{col 55}{space 3}0.053{col 63}{space 4}-2.570307{col 76}{space 3} .0174157
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 110 failures and 0 successes completely determined.{p_end}

{com}. imb weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(no_esc_matching)
{res}
{txt}Multivariate L1 distance: {res}.87132637

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res} .02235   .02235        0        0        0        0        0
{txt}         str_centcomm  {res} .25859   .45588        1        1        0        1        0
{txt}               parity  {res} .07719   .07719        0        0        0        0        0
{txt}             groupnum  {res} .21421  -.35461        0        0       -1        0       -1
{txt}          dyad_months  {res} .12263  -13.986        0       -3       -8      -19        0
{txt}                    t  {res} .27887  -36.533        0       -3      -20      -65     -106
{txt}
{com}. estimates store imb3_pre
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(no_esc_matching) autocuts(fd) 
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}206
{txt}Number of matched strata: {res}23

           {txt}  0    1
      All  {res}439  611
{txt}  Matched  {res} 83  126
{txt}Unmatched  {res}356  485


{txt}Multivariate L1 distance: {res}.45260141

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res}5.9e-17  6.9e-18        0        0        0        0        0
{txt}         str_centcomm  {res}2.2e-16  1.3e-15        0        0        0        0        0
{txt}               parity  {res}1.9e-16  5.6e-17        0        0        0        0        0
{txt}             groupnum  {res}8.3e-17  2.2e-16        0        0        0        0        0
{txt}          dyad_months  {res} .13073  -.77469       -1        1        0       -4        7
{txt}                    t  {res} .06944   1.6944        0        1        4       -1        0
{txt}
{com}. logit neg_DV no_esc_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-114.97247}  
Iteration 1:{space 3}log pseudolikelihood = {res:-83.088164}  
Iteration 2:{space 3}log pseudolikelihood = {res:-79.151073}  
Iteration 3:{space 3}log pseudolikelihood = {res:-78.905494}  
Iteration 4:{space 3}log pseudolikelihood = {res:-78.795828}  
Iteration 5:{space 3}log pseudolikelihood = {res:-78.617159}  
Iteration 6:{space 3}log pseudolikelihood = {res:-78.264705}  
Iteration 7:{space 3}log pseudolikelihood = {res:-77.914901}  
Iteration 8:{space 3}log pseudolikelihood = {res:-77.530591}  
Iteration 9:{space 3}log pseudolikelihood = {res:-77.522192}  
Iteration 10:{space 2}log pseudolikelihood = {res:-77.522163}  
Iteration 11:{space 2}log pseudolikelihood = {res:-77.522163}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       209
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     24.85
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-77.522163{txt}{col 49}Pseudo R2{col 67}= {res}    0.3257

{txt}{ralign 81:(Std. Err. adjusted for {res:21} clusters in dyadid)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         neg_DV{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
no_esc_matching {c |}{col 17}{res}{space 2}-1.684316{col 29}{space 2} .5394405{col 40}{space 1}   -3.12{col 49}{space 3}0.002{col 57}{space 4}  -2.7416{col 70}{space 3}-.6270321
{txt}{space 4}dyad_months {c |}{col 17}{res}{space 2} .0092362{col 29}{space 2} .0041755{col 40}{space 1}    2.21{col 49}{space 3}0.027{col 57}{space 4} .0010523{col 70}{space 3}   .01742
{txt}{space 14}t {c |}{col 17}{res}{space 2}-1.312593{col 29}{space 2} 1.013887{col 40}{space 1}   -1.29{col 49}{space 3}0.195{col 57}{space 4}-3.299776{col 70}{space 3}  .674589
{txt}{space 15} {c |}
{space 8}c.t#c.t {c |}{col 17}{res}{space 2} .1316537{col 29}{space 2} .1208758{col 40}{space 1}    1.09{col 49}{space 3}0.276{col 57}{space 4}-.1052584{col 70}{space 3} .3685658
{txt}{space 15} {c |}
{space 4}c.t#c.t#c.t {c |}{col 17}{res}{space 2}-.0044106{col 29}{space 2} .0041719{col 40}{space 1}   -1.06{col 49}{space 3}0.290{col 57}{space 4}-.0125873{col 70}{space 3} .0037662
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 2.665237{col 29}{space 2} 1.984213{col 40}{space 1}    1.34{col 49}{space 3}0.179{col 57}{space 4}-1.223749{col 70}{space 3} 6.554223
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 26 failures and 0 successes completely determined.{p_end}

{com}. estimates store m3t3
{txt}
{com}. 
. * Model 4, Table 3 (matching: control=de-escalation, no third-party; treatment= escalation, no third-party)
. logit neg_DV de_esc_matching_nothird weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-75.410833}  
Iteration 1:{space 3}log pseudolikelihood = {res:-62.505305}  
Iteration 2:{space 3}log pseudolikelihood = {res:-58.028627}  
Iteration 3:{space 3}log pseudolikelihood = {res:-57.545228}  
Iteration 4:{space 3}log pseudolikelihood = {res:-57.478866}  
Iteration 5:{space 3}log pseudolikelihood = {res:-57.462977}  
Iteration 6:{space 3}log pseudolikelihood = {res:-57.462479}  
Iteration 7:{space 3}log pseudolikelihood = {res:-57.462477}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       446
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    283.43
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-57.462477{txt}{col 49}Pseudo R2{col 67}= {res}    0.2380

{txt}{ralign 89:(Std. Err. adjusted for {res:21} clusters in dyadid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                 neg_DV{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching_nothird {c |}{col 25}{res}{space 2} -1.43696{col 37}{space 2} .7221042{col 48}{space 1}   -1.99{col 57}{space 3}0.047{col 65}{space 4}-2.852259{col 78}{space 3}-.0216623
{txt}{space 2}weak_con_last_neg_dum {c |}{col 25}{res}{space 2} 1.067034{col 37}{space 2} .5486104{col 48}{space 1}    1.94{col 57}{space 3}0.052{col 65}{space 4} -.008223{col 78}{space 3}  2.14229
{txt}{space 11}str_centcomm {c |}{col 25}{res}{space 2}-.2594771{col 37}{space 2}  .237573{col 48}{space 1}   -1.09{col 57}{space 3}0.275{col 65}{space 4}-.7251117{col 78}{space 3} .2061575
{txt}{space 17}parity {c |}{col 25}{res}{space 2} 1.763977{col 37}{space 2} .9190079{col 48}{space 1}    1.92{col 57}{space 3}0.055{col 65}{space 4}-.0372456{col 78}{space 3} 3.565199
{txt}{space 15}groupnum {c |}{col 25}{res}{space 2} .4331499{col 37}{space 2} .2881422{col 48}{space 1}    1.50{col 57}{space 3}0.133{col 65}{space 4}-.1315985{col 78}{space 3} .9978983
{txt}{space 12}dyad_months {c |}{col 25}{res}{space 2}-.0007454{col 37}{space 2} .0056398{col 48}{space 1}   -0.13{col 57}{space 3}0.895{col 65}{space 4}-.0117993{col 78}{space 3} .0103084
{txt}{space 22}t {c |}{col 25}{res}{space 2}-.1359606{col 37}{space 2} .2177513{col 48}{space 1}   -0.62{col 57}{space 3}0.532{col 65}{space 4}-.5627453{col 78}{space 3} .2908241
{txt}{space 23} {c |}
{space 16}c.t#c.t {c |}{col 25}{res}{space 2} .0029325{col 37}{space 2} .0115655{col 48}{space 1}    0.25{col 57}{space 3}0.800{col 65}{space 4}-.0197356{col 78}{space 3} .0256005
{txt}{space 23} {c |}
{space 12}c.t#c.t#c.t {c |}{col 25}{res}{space 2}-.0000403{col 37}{space 2} .0001463{col 48}{space 1}   -0.28{col 57}{space 3}0.783{col 65}{space 4} -.000327{col 78}{space 3} .0002465
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}  -1.7484{col 37}{space 2} 1.076481{col 48}{space 1}   -1.62{col 57}{space 3}0.104{col 65}{space 4}-3.858263{col 78}{space 3} .3614633
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. imb weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(de_esc_matching_nothird)
{res}
{txt}Multivariate L1 distance: {res}.86961409

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res} .08933  -.08933        0        0        0        0        0
{txt}         str_centcomm  {res} .22998   .29293        1        0        0        0        0
{txt}               parity  {res} .11128   .11128        0        0        0        0        0
{txt}             groupnum  {res} .14443  -.07302        0        0        0        0       -1
{txt}          dyad_months  {res} .18139  -3.4802        2        7        1      -24       14
{txt}                    t  {res} .13748   .69735        0        3       -3        2       -2
{txt}
{com}. estimates store imb4_pre
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months  t  if e(sample), treatment(de_esc_matching_nothird) autocut(fd)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}107
{txt}Number of matched strata: {res}11

           {txt}  0    1
      All  {res}157  289
{txt}  Matched  {res} 38   46
{txt}Unmatched  {res}119  243


{txt}Multivariate L1 distance: {res}.36490683

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 1.1e-16         0         0         0         0         0         0
{txt}         str_centcomm  {res} 5.6e-17  -4.4e-16         0         0         0         0         0
{txt}               parity  {res}       0         0         0         0         0         0         0
{txt}             groupnum  {res} 2.2e-16  -1.3e-15         0         0         0         0         0
{txt}          dyad_months  {res}   .2073    5.5707         2         7         5         8        13
{txt}                    t  {res} 9.0e-17   -.49457         0        -2         0        -1        -3
{txt}
{com}. logit neg_DV de_esc_matching_nothird dyad_months c.t##c.t##c.t [iweight=cem_weights] , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-22.982959}  
Iteration 1:{space 3}log pseudolikelihood = {res:-19.133707}  
Iteration 2:{space 3}log pseudolikelihood = {res:-15.445636}  
Iteration 3:{space 3}log pseudolikelihood = {res:-14.940278}  
Iteration 4:{space 3}log pseudolikelihood = {res: -14.54716}  
Iteration 5:{space 3}log pseudolikelihood = {res:-14.045217}  
Iteration 6:{space 3}log pseudolikelihood = {res:-13.944428}  
Iteration 7:{space 3}log pseudolikelihood = {res:-13.940447}  
Iteration 8:{space 3}log pseudolikelihood = {res:-13.940435}  
Iteration 9:{space 3}log pseudolikelihood = {res:-13.940435}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}        84
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     52.34
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-13.940435{txt}{col 49}Pseudo R2{col 67}= {res}    0.3934

{txt}{ralign 89:(Std. Err. adjusted for {res:12} clusters in dyadid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                 neg_DV{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching_nothird {c |}{col 25}{res}{space 2}-2.099503{col 37}{space 2} .8342923{col 48}{space 1}   -2.52{col 57}{space 3}0.012{col 65}{space 4}-3.734686{col 78}{space 3}-.4643202
{txt}{space 12}dyad_months {c |}{col 25}{res}{space 2} .0911681{col 37}{space 2} .0344043{col 48}{space 1}    2.65{col 57}{space 3}0.008{col 65}{space 4}  .023737{col 78}{space 3} .1585993
{txt}{space 22}t {c |}{col 25}{res}{space 2}-3.740852{col 37}{space 2} 1.998998{col 48}{space 1}   -1.87{col 57}{space 3}0.061{col 65}{space 4}-7.658817{col 78}{space 3} .1771129
{txt}{space 23} {c |}
{space 16}c.t#c.t {c |}{col 25}{res}{space 2} .4997015{col 37}{space 2}  .256104{col 48}{space 1}    1.95{col 57}{space 3}0.051{col 65}{space 4}-.0022531{col 78}{space 3} 1.001656
{txt}{space 23} {c |}
{space 12}c.t#c.t#c.t {c |}{col 25}{res}{space 2}-.0200847{col 37}{space 2} .0095395{col 48}{space 1}   -2.11{col 57}{space 3}0.035{col 65}{space 4}-.0387818{col 78}{space 3}-.0013877
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2} 2.661925{col 37}{space 2}   2.5189{col 48}{space 1}    1.06{col 57}{space 3}0.291{col 65}{space 4}-2.275029{col 78}{space 3} 7.598878
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 17 failures and 0 successes completely determined.{p_end}

{com}. estimates store m4t3
{txt}
{com}. 
. * Model 5, Table 3 (matching: control=de-escalation with third-party; treatment= escalation with third-party)
. logit neg_DV de_esc_matching_third weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-183.20275}  
Iteration 1:{space 3}log pseudolikelihood = {res:-177.47672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-177.32396}  
Iteration 3:{space 3}log pseudolikelihood = {res:-177.32388}  
Iteration 4:{space 3}log pseudolikelihood = {res:-177.32388}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       374
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}     37.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-177.32388{txt}{col 49}Pseudo R2{col 67}= {res}    0.0321

{txt}{ralign 87:(Std. Err. adjusted for {res:27} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching_third {c |}{col 23}{res}{space 2}-.0518103{col 35}{space 2} .2274453{col 46}{space 1}   -0.23{col 55}{space 3}0.820{col 63}{space 4}-.4975949{col 76}{space 3} .3939743
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2}   .36246{col 35}{space 2} .3285561{col 46}{space 1}    1.10{col 55}{space 3}0.270{col 63}{space 4}-.2814981{col 76}{space 3} 1.006418
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.1391601{col 35}{space 2} .2291323{col 46}{space 1}   -0.61{col 55}{space 3}0.544{col 63}{space 4}-.5882511{col 76}{space 3} .3099309
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .4052729{col 35}{space 2} .2815325{col 46}{space 1}    1.44{col 55}{space 3}0.150{col 63}{space 4}-.1465207{col 76}{space 3} .9570665
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}-.2503624{col 35}{space 2} .2856659{col 46}{space 1}   -0.88{col 55}{space 3}0.381{col 63}{space 4}-.8102573{col 76}{space 3} .3095326
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0009392{col 35}{space 2} .0034201{col 46}{space 1}   -0.27{col 55}{space 3}0.784{col 63}{space 4}-.0076425{col 76}{space 3} .0057641
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.0963577{col 35}{space 2} .0836064{col 46}{space 1}   -1.15{col 55}{space 3}0.249{col 63}{space 4}-.2602232{col 76}{space 3} .0675078
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0017096{col 35}{space 2} .0052332{col 46}{space 1}    0.33{col 55}{space 3}0.744{col 63}{space 4}-.0085473{col 76}{space 3} .0119665
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}  .000011{col 35}{space 2} .0000843{col 46}{space 1}    0.13{col 55}{space 3}0.896{col 63}{space 4}-.0001542{col 76}{space 3} .0001763
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.2818335{col 35}{space 2} .8129676{col 46}{space 1}   -0.35{col 55}{space 3}0.729{col 63}{space 4}-1.875221{col 76}{space 3} 1.311554
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. imb weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(de_esc_matching_third)
{res}
{txt}Multivariate L1 distance: {res}.75341055

{txt}Univariate imbalance:

                            L1     mean      min      25%      50%      75%      max
weak_con_last_neg_dum  {res} .02131  -.02131        0        0        0       -1        0
{txt}         str_centcomm  {res} .17237   .26486        1        0        0        1        0
{txt}               parity  {res} .05566   .05566        0        0        0        0        0
{txt}             groupnum  {res} .21307    .0151        0        0        0        0        0
{txt}          dyad_months  {res} .22437   14.652        3        8       11       18       20
{txt}                    t  {res} .16983   4.1465        0        0        2        6        0
{txt}
{com}. estimates store imb5_pre
{txt}
{com}. cem weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months  t  if e(sample), treatment(de_esc_matching_third) autocut(fd)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}164
{txt}Number of matched strata: {res}32

           {txt}  0    1
      All  {res}189  185
{txt}  Matched  {res} 86   64
{txt}Unmatched  {res}103  121


{txt}Multivariate L1 distance: {res}.24869792

{txt}Univariate imbalance:

                             L1      mean       min       25%       50%       75%       max
weak_con_last_neg_dum  {res} 2.8e-17  -5.6e-17         0         0         0         0         0
{txt}         str_centcomm  {res} 1.8e-16  -8.9e-16         0         0         0        -1         0
{txt}               parity  {res} 8.3e-17  -5.6e-17         0         0         0         0         0
{txt}             groupnum  {res} 4.2e-17         0         0         0         0         0         0
{txt}          dyad_months  {res}  .08984    .84115         2         0        -4       -11         8
{txt}                    t  {res}  .02865    .02995         0         0         0         0         0
{txt}
{com}. logit neg_DV de_esc_matching_third dyad_months c.t##c.t##c.t [iweight=cem_weights] , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-88.078515}  
Iteration 1:{space 3}log pseudolikelihood = {res:-84.967693}  
Iteration 2:{space 3}log pseudolikelihood = {res:-84.920011}  
Iteration 3:{space 3}log pseudolikelihood = {res: -84.91997}  
Iteration 4:{space 3}log pseudolikelihood = {res: -84.91997}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       150
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}      4.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4715
{txt}Log pseudolikelihood = {res} -84.91997{txt}{col 49}Pseudo R2{col 67}= {res}    0.0359

{txt}{ralign 87:(Std. Err. adjusted for {res:18} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching_third {c |}{col 23}{res}{space 2} -.524451{col 35}{space 2} .3904986{col 46}{space 1}   -1.34{col 55}{space 3}0.179{col 63}{space 4}-1.289814{col 76}{space 3} .2409122
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0076963{col 35}{space 2} .0051371{col 46}{space 1}   -1.50{col 55}{space 3}0.134{col 63}{space 4}-.0177648{col 76}{space 3} .0023721
{txt}{space 20}t {c |}{col 23}{res}{space 2} 1.100135{col 35}{space 2} .9801543{col 46}{space 1}    1.12{col 55}{space 3}0.262{col 63}{space 4}-.8209326{col 76}{space 3} 3.021202
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2}-.1534431{col 35}{space 2} .1483049{col 46}{space 1}   -1.03{col 55}{space 3}0.301{col 63}{space 4}-.4441153{col 76}{space 3} .1372292
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2} .0057991{col 35}{space 2} .0063948{col 46}{space 1}    0.91{col 55}{space 3}0.364{col 63}{space 4}-.0067345{col 76}{space 3} .0183327
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-2.166887{col 35}{space 2} 1.642264{col 46}{space 1}   -1.32{col 55}{space 3}0.187{col 63}{space 4}-5.385666{col 76}{space 3} 1.051891
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store m5t3            
{txt}
{com}. 
. * Table 3 *
. #delimit;
{txt}delimiter now ;
{com}. esttab m1t3 m2t3 m3t3 m4t3 m5t3 using table3.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table 3: CEM Matching Results) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. *p<0.05. Variables for which balance was achieved are not included as controls.) 
>   nonumbers mtitles("Model 1 Treatment: Escalation Control: De-Escalation" "Model 2 Treatment: No Change Control: De-Escalation" "Model 3 Treatment: Escalation Control: No Change" "Model 4 Treatment: Escalation, No Third-Party Control: De-Escalation, No Third-Party" "Model 5 Treatment: Escalation, Third-Party Control: De-Escalation, Third-Party")  
>   star(* 0.05) ;
{res}{txt}(output written to {browse  `"table3.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
. 
. 
. *** Footnote 29: matching on Third-party Involvment 
. * Model 1, Table 3 (matching: control=de-escalation; treatment=escalation)
. logit neg_DV de_esc_matching thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-283.72418}  
Iteration 1:{space 3}log pseudolikelihood = {res: -250.6431}  
Iteration 2:{space 3}log pseudolikelihood = {res:-244.28538}  
Iteration 3:{space 3}log pseudolikelihood = {res:-243.99097}  
Iteration 4:{space 3}log pseudolikelihood = {res:-243.97299}  
Iteration 5:{space 3}log pseudolikelihood = {res:-243.97299}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       820
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    106.07
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-243.97299{txt}{col 49}Pseudo R2{col 67}= {res}    0.1401

{txt}{ralign 88:(Std. Err. adjusted for {res:30} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}de_esc_matching {c |}{col 24}{res}{space 2}-.3224846{col 36}{space 2} .1938361{col 47}{space 1}   -1.66{col 56}{space 3}0.096{col 64}{space 4}-.7023964{col 77}{space 3} .0574272
{txt}thirdparty_involvement {c |}{col 24}{res}{space 2} 1.220599{col 36}{space 2} .4834895{col 47}{space 1}    2.52{col 56}{space 3}0.012{col 64}{space 4}  .272977{col 77}{space 3} 2.168221
{txt}{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2} .5917645{col 36}{space 2} .3159936{col 47}{space 1}    1.87{col 56}{space 3}0.061{col 64}{space 4}-.0275715{col 77}{space 3} 1.211101
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2}-.0944263{col 36}{space 2} .1723578{col 47}{space 1}   -0.55{col 56}{space 3}0.584{col 64}{space 4}-.4322414{col 77}{space 3} .2433889
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .5560347{col 36}{space 2} .2401668{col 47}{space 1}    2.32{col 56}{space 3}0.021{col 64}{space 4} .0853164{col 77}{space 3} 1.026753
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2} -.046926{col 36}{space 2} .2471121{col 47}{space 1}   -0.19{col 56}{space 3}0.849{col 64}{space 4}-.5312568{col 77}{space 3} .4374048
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0009012{col 36}{space 2} .0027866{col 47}{space 1}   -0.32{col 56}{space 3}0.746{col 64}{space 4}-.0063629{col 77}{space 3} .0045605
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.1668016{col 36}{space 2} .0740779{col 47}{space 1}   -2.25{col 56}{space 3}0.024{col 64}{space 4}-.3119916{col 77}{space 3}-.0216115
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2} .0058762{col 36}{space 2} .0034839{col 47}{space 1}    1.69{col 56}{space 3}0.092{col 64}{space 4}-.0009522{col 77}{space 3} .0127045
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000642{col 36}{space 2} .0000394{col 47}{space 1}   -1.63{col 56}{space 3}0.103{col 64}{space 4}-.0001415{col 77}{space 3} .0000131
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}-1.645426{col 36}{space 2} .6720001{col 47}{space 1}   -2.45{col 56}{space 3}0.014{col 64}{space 4}-2.962522{col 77}{space 3}  -.32833
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. imb weak_con_last_neg_dum thirdparty_involvement str_centcomm parity groupnum dyad_months t if e(sample), treatment(de_esc_matching)
{res}
{txt}Multivariate L1 distance: {res}.82772371

{txt}Univariate imbalance:

                             L1     mean      min      25%      50%      75%      max
 weak_con_last_neg_dum  {res} .08417  -.08417        0        0        0        0        0
{txt}thirdparty_involvement  {res} .15595  -.15595        0        0       -1        0        0
{txt}          str_centcomm  {res} .10405   .26112        1        0        0        0        0
{txt}                parity  {res} .07901   .07901        0        0        0        0        0
{txt}              groupnum  {res}  .1954  -.06708        0        0       -1        0       -1
{txt}           dyad_months  {res} .16004   6.5474        3       11       17        1       20
{txt}                     t  {res} .19139   4.3071        0        2        5        7       -2
{txt}
{com}. cem weak_con_last_neg_dum (#0) thirdparty_involvement (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(de_esc_matching) autocuts(fd)
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}264
{txt}Number of matched strata: {res}37

           {txt}  0    1
      All  {res}346  474
{txt}  Matched  {res}132  117
{txt}Unmatched  {res}214  357


{txt}Multivariate L1 distance: {res}.34595035

{txt}Univariate imbalance:

                              L1      mean       min       25%       50%       75%       max
 weak_con_last_neg_dum  {res} 6.9e-17  -2.8e-17         0         0         0         0         0
{txt}thirdparty_involvement  {res} 2.8e-17         0         0         0         0         0         0
{txt}          str_centcomm  {res} 2.8e-16  -8.9e-16         0         0         0         0         0
{txt}                parity  {res} 1.9e-16  -5.6e-17         0         0         0         0         0
{txt}              groupnum  {res} 1.2e-16  -2.2e-16         0         0         0         0         0
{txt}           dyad_months  {res}  .13527    2.1461         2         3         5         1         5
{txt}                     t  {res}  .03775     .2188         0         1         1         0        -3
{txt}
{com}. logit neg_DV de_esc_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-127.88957}  
Iteration 1:{space 3}log pseudolikelihood = {res:-117.36936}  
Iteration 2:{space 3}log pseudolikelihood = {res:-116.01258}  
Iteration 3:{space 3}log pseudolikelihood = {res:-115.28286}  
Iteration 4:{space 3}log pseudolikelihood = {res:-114.79604}  
Iteration 5:{space 3}log pseudolikelihood = {res:-114.71847}  
Iteration 6:{space 3}log pseudolikelihood = {res:-114.71625}  
Iteration 7:{space 3}log pseudolikelihood = {res:-114.71624}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       249
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     12.73
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0261
{txt}Log pseudolikelihood = {res}-114.71624{txt}{col 49}Pseudo R2{col 67}= {res}    0.1030

{txt}{ralign 81:(Std. Err. adjusted for {res:20} clusters in dyadid)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         neg_DV{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
de_esc_matching {c |}{col 17}{res}{space 2}-.9442232{col 29}{space 2} .4524076{col 40}{space 1}   -2.09{col 49}{space 3}0.037{col 57}{space 4}-1.830926{col 70}{space 3}-.0575207
{txt}{space 4}dyad_months {c |}{col 17}{res}{space 2} .0007762{col 29}{space 2} .0051318{col 40}{space 1}    0.15{col 49}{space 3}0.880{col 57}{space 4}-.0092819{col 70}{space 3} .0108343
{txt}{space 14}t {c |}{col 17}{res}{space 2} -.677549{col 29}{space 2} .6503432{col 40}{space 1}   -1.04{col 49}{space 3}0.297{col 57}{space 4}-1.952198{col 70}{space 3} .5971002
{txt}{space 15} {c |}
{space 8}c.t#c.t {c |}{col 17}{res}{space 2} .0700016{col 29}{space 2} .0699817{col 40}{space 1}    1.00{col 49}{space 3}0.317{col 57}{space 4}  -.06716{col 70}{space 3} .2071633
{txt}{space 15} {c |}
{space 4}c.t#c.t#c.t {c |}{col 17}{res}{space 2}-.0022448{col 29}{space 2} .0020328{col 40}{space 1}   -1.10{col 49}{space 3}0.269{col 57}{space 4} -.006229{col 70}{space 3} .0017394
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} .7133688{col 29}{space 2} 1.493043{col 40}{space 1}    0.48{col 49}{space 3}0.633{col 57}{space 4}-2.212942{col 70}{space 3} 3.639679
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 9 failures and 0 successes completely determined.{p_end}

{com}. * Model 2, Table 3 (matching: control=de-escalation; treatment=no change)
. logit neg_DV deesc_no_matching thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.46171}  
Iteration 1:{space 3}log pseudolikelihood = {res:-203.76906}  
Iteration 2:{space 3}log pseudolikelihood = {res:-194.25965}  
Iteration 3:{space 3}log pseudolikelihood = {res:-193.31027}  
Iteration 4:{space 3}log pseudolikelihood = {res:-192.96305}  
Iteration 5:{space 3}log pseudolikelihood = {res:-192.50905}  
Iteration 6:{space 3}log pseudolikelihood = {res:-192.08383}  
Iteration 7:{space 3}log pseudolikelihood = {res:-191.92287}  
Iteration 8:{space 3}log pseudolikelihood = {res:-191.91064}  
Iteration 9:{space 3}log pseudolikelihood = {res:-191.91059}  
Iteration 10:{space 2}log pseudolikelihood = {res:-191.91059}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       705
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     85.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-191.91059{txt}{col 49}Pseudo R2{col 67}= {res}    0.1744

{txt}{ralign 88:(Std. Err. adjusted for {res:32} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}deesc_no_matching {c |}{col 24}{res}{space 2}-.1495032{col 36}{space 2} .2693223{col 47}{space 1}   -0.56{col 56}{space 3}0.579{col 64}{space 4}-.6773652{col 77}{space 3} .3783587
{txt}thirdparty_involvement {c |}{col 24}{res}{space 2} .6554953{col 36}{space 2} .4727209{col 47}{space 1}    1.39{col 56}{space 3}0.166{col 64}{space 4}-.2710207{col 77}{space 3} 1.582011
{txt}{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2} .4203162{col 36}{space 2} .2807897{col 47}{space 1}    1.50{col 56}{space 3}0.134{col 64}{space 4}-.1300215{col 77}{space 3}  .970654
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2}-.0952105{col 36}{space 2} .0950718{col 47}{space 1}   -1.00{col 56}{space 3}0.317{col 64}{space 4}-.2815478{col 77}{space 3} .0911267
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .5356267{col 36}{space 2} .3377276{col 47}{space 1}    1.59{col 56}{space 3}0.113{col 64}{space 4}-.1263073{col 77}{space 3} 1.197561
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2}-.1941143{col 36}{space 2} .2039256{col 47}{space 1}   -0.95{col 56}{space 3}0.341{col 64}{space 4}-.5938012{col 77}{space 3} .2055727
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0000606{col 36}{space 2} .0023469{col 47}{space 1}   -0.03{col 56}{space 3}0.979{col 64}{space 4}-.0046604{col 77}{space 3} .0045392
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.1758818{col 36}{space 2} .0702797{col 47}{space 1}   -2.50{col 56}{space 3}0.012{col 64}{space 4}-.3136275{col 77}{space 3}-.0381361
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2}  .004986{col 36}{space 2} .0031406{col 47}{space 1}    1.59{col 56}{space 3}0.112{col 64}{space 4}-.0011696{col 77}{space 3} .0111415
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000469{col 36}{space 2} .0000352{col 47}{space 1}   -1.33{col 56}{space 3}0.183{col 64}{space 4} -.000116{col 77}{space 3} .0000221
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} -.861507{col 36}{space 2} .7522589{col 47}{space 1}   -1.15{col 56}{space 3}0.252{col 64}{space 4}-2.335907{col 77}{space 3} .6128934
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 79 failures and 0 successes completely determined.{p_end}

{com}. imb thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(deesc_no_matching)
{res}
{txt}Multivariate L1 distance: {res}.88916708

{txt}Univariate imbalance:

                             L1     mean      min      25%      50%      75%      max
thirdparty_involvement  {res} .36797  -.36797        0        0       -1       -1        0
{txt} weak_con_last_neg_dum  {res} .02682  -.02682        0        0        0        0        0
{txt}          str_centcomm  {res} .23398    -.268        0        0        0       -1        0
{txt}                parity  {res} .01828  -.01828        0        0        0        0        0
{txt}              groupnum  {res} .18311   .25626        0        0        0        0        0
{txt}           dyad_months  {res}  .1968   24.329       -2       25       34       23       37
{txt}                     t  {res} .39797   42.042        0        7       27       73      104
{txt}
{com}. cem thirdparty_involvement (#0) weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(deesc_no_matching) autocuts(fd) 
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}161
{txt}Number of matched strata: {res}18

           {txt}  0    1
      All  {res}346  359
{txt}  Matched  {res} 60   42
{txt}Unmatched  {res}286  317


{txt}Multivariate L1 distance: {res}.41441971

{txt}Univariate imbalance:

                              L1      mean       min       25%       50%       75%       max
thirdparty_involvement  {res}       0         0         0         0         0         0         0
{txt} weak_con_last_neg_dum  {res} 6.9e-18  -1.4e-17         0         0         0         0         0
{txt}          str_centcomm  {res} 6.2e-17  -4.4e-16         0         0         0         0         0
{txt}                parity  {res} 1.7e-18  -3.5e-18         0         0         0         0         0
{txt}              groupnum  {res} 3.1e-17  -1.6e-15         0         0         0         0         0
{txt}           dyad_months  {res}  .07143    1.0811        -3         0        11         4         0
{txt}                     t  {res}  .28517   -.00488         0        -1        -3         5        -4
{txt}
{com}. logit neg_DV deesc_no_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-48.827826}  
Iteration 1:{space 3}log pseudolikelihood = {res:-44.224006}  
Iteration 2:{space 3}log pseudolikelihood = {res:-43.413393}  
Iteration 3:{space 3}log pseudolikelihood = {res:-43.051393}  
Iteration 4:{space 3}log pseudolikelihood = {res:-42.474823}  
Iteration 5:{space 3}log pseudolikelihood = {res:-42.293662}  
Iteration 6:{space 3}log pseudolikelihood = {res:-42.281359}  
Iteration 7:{space 3}log pseudolikelihood = {res:-42.281333}  
Iteration 8:{space 3}log pseudolikelihood = {res:-42.281333}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       102
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}      4.39
{txt}{col 49}Prob > chi2{col 67}= {res}    0.4941
{txt}Log pseudolikelihood = {res}-42.281333{txt}{col 49}Pseudo R2{col 67}= {res}    0.1341

{txt}{ralign 83:(Std. Err. adjusted for {res:19} clusters in dyadid)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}           neg_DV{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
deesc_no_matching {c |}{col 19}{res}{space 2} .9676932{col 31}{space 2} .6701601{col 42}{space 1}    1.44{col 51}{space 3}0.149{col 59}{space 4}-.3457965{col 72}{space 3} 2.281183
{txt}{space 6}dyad_months {c |}{col 19}{res}{space 2}-.0011638{col 31}{space 2}  .003812{col 42}{space 1}   -0.31{col 51}{space 3}0.760{col 59}{space 4}-.0086351{col 72}{space 3} .0063076
{txt}{space 16}t {c |}{col 19}{res}{space 2} -.611089{col 31}{space 2} .7153038{col 42}{space 1}   -0.85{col 51}{space 3}0.393{col 59}{space 4}-2.013059{col 72}{space 3} .7908808
{txt}{space 17} {c |}
{space 10}c.t#c.t {c |}{col 19}{res}{space 2} .1058256{col 31}{space 2} .1102394{col 42}{space 1}    0.96{col 51}{space 3}0.337{col 59}{space 4}-.1102397{col 72}{space 3} .3218908
{txt}{space 17} {c |}
{space 6}c.t#c.t#c.t {c |}{col 19}{res}{space 2}-.0051826{col 31}{space 2} .0050964{col 42}{space 1}   -1.02{col 51}{space 3}0.309{col 59}{space 4}-.0151713{col 72}{space 3} .0048061
{txt}{space 17} {c |}
{space 12}_cons {c |}{col 19}{res}{space 2}-.5160922{col 31}{space 2} 1.369301{col 42}{space 1}   -0.38{col 51}{space 3}0.706{col 59}{space 4}-3.199872{col 72}{space 3} 2.167688
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 5 failures and 0 successes completely determined.{p_end}

{com}. * Model 3, Table 3 (matching: control=no change; treatment=escalation)
. logit neg_DV no_esc_matching thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-215.62665}  
Iteration 1:{space 3}log pseudolikelihood = {res:-189.57759}  
Iteration 2:{space 3}log pseudolikelihood = {res:-175.73443}  
Iteration 3:{space 3}log pseudolikelihood = {res:-174.93685}  
Iteration 4:{space 3}log pseudolikelihood = {res:-174.33804}  
Iteration 5:{space 3}log pseudolikelihood = {res:-173.25797}  
Iteration 6:{space 3}log pseudolikelihood = {res:-172.62237}  
Iteration 7:{space 3}log pseudolikelihood = {res:-172.43963}  
Iteration 8:{space 3}log pseudolikelihood = {res:-172.43182}  
Iteration 9:{space 3}log pseudolikelihood = {res:-172.43177}  
Iteration 10:{space 2}log pseudolikelihood = {res:-172.43177}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       833
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     52.50
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-172.43177{txt}{col 49}Pseudo R2{col 67}= {res}    0.2003

{txt}{ralign 88:(Std. Err. adjusted for {res:31} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}no_esc_matching {c |}{col 24}{res}{space 2}-.2499581{col 36}{space 2} .2766453{col 47}{space 1}   -0.90{col 56}{space 3}0.366{col 64}{space 4}-.7921729{col 77}{space 3} .2922567
{txt}thirdparty_involvement {c |}{col 24}{res}{space 2} 1.534387{col 36}{space 2} .3441297{col 47}{space 1}    4.46{col 56}{space 3}0.000{col 64}{space 4} .8599048{col 77}{space 3} 2.208868
{txt}{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2}  .261306{col 36}{space 2} .3674831{col 47}{space 1}    0.71{col 56}{space 3}0.477{col 64}{space 4}-.4589478{col 77}{space 3} .9815597
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2}-.1527623{col 36}{space 2} .1390177{col 47}{space 1}   -1.10{col 56}{space 3}0.272{col 64}{space 4}-.4252321{col 77}{space 3} .1197074
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .3692435{col 36}{space 2} .3596188{col 47}{space 1}    1.03{col 56}{space 3}0.305{col 64}{space 4}-.3355964{col 77}{space 3} 1.074083
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2}  .020299{col 36}{space 2} .1683917{col 47}{space 1}    0.12{col 56}{space 3}0.904{col 64}{space 4}-.3097426{col 77}{space 3} .3503406
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0017779{col 36}{space 2}  .003015{col 47}{space 1}   -0.59{col 56}{space 3}0.555{col 64}{space 4}-.0076871{col 77}{space 3} .0041314
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.2056083{col 36}{space 2} .0620754{col 47}{space 1}   -3.31{col 56}{space 3}0.001{col 64}{space 4}-.3272739{col 77}{space 3}-.0839428
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2} .0065093{col 36}{space 2} .0024268{col 47}{space 1}    2.68{col 56}{space 3}0.007{col 64}{space 4} .0017529{col 77}{space 3} .0112657
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000603{col 36}{space 2} .0000255{col 47}{space 1}   -2.36{col 56}{space 3}0.018{col 64}{space 4}-.0001104{col 77}{space 3}-.0000103
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} -1.55043{col 36}{space 2} .5796795{col 47}{space 1}   -2.67{col 56}{space 3}0.007{col 64}{space 4}-2.686581{col 77}{space 3}-.4142788
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 81 failures and 0 successes completely determined.{p_end}

{com}. imb thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t if e(sample), treatment(no_esc_matching)
{res}
{txt}Multivariate L1 distance: {res}.91610545

{txt}Univariate imbalance:

                             L1     mean      min      25%      50%      75%      max
thirdparty_involvement  {res} .21202   .21202        0        0        0        1        0
{txt} weak_con_last_neg_dum  {res} .05734  -.05734        0        0        0        0        0
{txt}          str_centcomm  {res} .33692   .52911        1        0        0        1        0
{txt}                parity  {res} .09729   .09729        0        0        0        0        0
{txt}              groupnum  {res} .17932  -.32334        0        0       -1        0       -1
{txt}           dyad_months  {res} .18995  -17.782        5      -14      -17      -22      -17
{txt}                     t  {res} .36222  -37.735        0       -5      -22      -66     -106
{txt}
{com}. cem thirdparty_involvement weak_con_last_neg_dum (#0) str_centcomm (#0) parity (#0) groupnum (#0) dyad_months t if e(sample), treatment(no_esc_matching) autocuts(fd) 
{txt}(using the scott break method for imbalance)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}203
{txt}Number of matched strata: {res}16

           {txt}  0    1
      All  {res}359  474
{txt}  Matched  {res} 47   57
{txt}Unmatched  {res}312  417


{txt}Multivariate L1 distance: {res}.54385965

{txt}Univariate imbalance:

                             L1     mean      min      25%      50%      75%      max
thirdparty_involvement  {res}2.8e-17  5.6e-17        0        0        0        0        0
{txt} weak_con_last_neg_dum  {res}5.6e-17        0        0        0        0        0        0
{txt}          str_centcomm  {res}8.3e-17  4.4e-16        0        0        0        0        0
{txt}                parity  {res}5.6e-17        0        0        0        0        0        0
{txt}              groupnum  {res}8.7e-17  6.7e-16        0        0        0        0        0
{txt}           dyad_months  {res} .29825  -1.2354        3        2        5      -12      -10
{txt}                     t  {res} .03509   1.8713        0        1        5       -1        2
{txt}
{com}. logit neg_DV no_esc_matching dyad_months c.t##c.t##c.t [iweight=cem_weights], cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-61.987357}  
Iteration 1:{space 3}log pseudolikelihood = {res:-36.461517}  
Iteration 2:{space 3}log pseudolikelihood = {res:-32.695418}  
Iteration 3:{space 3}log pseudolikelihood = {res:-32.238857}  
Iteration 4:{space 3}log pseudolikelihood = {res: -32.15169}  
Iteration 5:{space 3}log pseudolikelihood = {res:-32.126065}  
Iteration 6:{space 3}log pseudolikelihood = {res:-32.105399}  
Iteration 7:{space 3}log pseudolikelihood = {res:-31.424018}  
Iteration 8:{space 3}log pseudolikelihood = {res:-31.200284}  
Iteration 9:{space 3}log pseudolikelihood = {res:  -31.1697}  
Iteration 10:{space 2}log pseudolikelihood = {res:-31.168961}  
Iteration 11:{space 2}log pseudolikelihood = {res:-31.168959}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       104
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     39.50
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-31.168959{txt}{col 49}Pseudo R2{col 67}= {res}    0.4972

{txt}{ralign 81:(Std. Err. adjusted for {res:18} clusters in dyadid)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         neg_DV{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
no_esc_matching {c |}{col 17}{res}{space 2}-1.933723{col 29}{space 2} .7064002{col 40}{space 1}   -2.74{col 49}{space 3}0.006{col 57}{space 4}-3.318242{col 70}{space 3}-.5492045
{txt}{space 4}dyad_months {c |}{col 17}{res}{space 2}-.0171846{col 29}{space 2}  .026412{col 40}{space 1}   -0.65{col 49}{space 3}0.515{col 57}{space 4}-.0689512{col 70}{space 3}  .034582
{txt}{space 14}t {c |}{col 17}{res}{space 2}-2.770164{col 29}{space 2} 1.728637{col 40}{space 1}   -1.60{col 49}{space 3}0.109{col 57}{space 4}-6.158229{col 70}{space 3} .6179017
{txt}{space 15} {c |}
{space 8}c.t#c.t {c |}{col 17}{res}{space 2} .3138901{col 29}{space 2} .2530088{col 40}{space 1}    1.24{col 49}{space 3}0.215{col 57}{space 4}-.1819981{col 70}{space 3} .8097783
{txt}{space 15} {c |}
{space 4}c.t#c.t#c.t {c |}{col 17}{res}{space 2}-.0111471{col 29}{space 2} .0099185{col 40}{space 1}   -1.12{col 49}{space 3}0.261{col 57}{space 4}-.0305869{col 70}{space 3} .0082927
{txt}{space 15} {c |}
{space 10}_cons {c |}{col 17}{res}{space 2} 6.358429{col 29}{space 2} 2.404573{col 40}{space 1}    2.64{col 49}{space 3}0.008{col 57}{space 4} 1.645553{col 70}{space 3} 11.07131
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 11 failures and 0 successes completely determined.{p_end}

{com}. 
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.         
.                 
. **********************************************************
. **********************************************************
. *** Analyses Presented in the Supplemental Information ***
. **********************************************************
. **********************************************************
. 
. *****************
. *****************
. *** Figure A1 ***
. *****************
. *****************
. collapse (sum) neg_onset, by(dyadid dyad_startyr)
{txt}
{com}. tab neg_onset if dyad_startyr>1989

      {txt}(sum) {c |}
  neg_onset {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         34       39.08       39.08
{txt}          1 {c |}{res}         21       24.14       63.22
{txt}          2 {c |}{res}         14       16.09       79.31
{txt}          3 {c |}{res}          2        2.30       81.61
{txt}          4 {c |}{res}          6        6.90       88.51
{txt}          5 {c |}{res}          1        1.15       89.66
{txt}          6 {c |}{res}          2        2.30       91.95
{txt}          7 {c |}{res}          1        1.15       93.10
{txt}          8 {c |}{res}          1        1.15       94.25
{txt}          9 {c |}{res}          1        1.15       95.40
{txt}         10 {c |}{res}          2        2.30       97.70
{txt}         11 {c |}{res}          1        1.15       98.85
{txt}         16 {c |}{res}          1        1.15      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         87      100.00
{txt}
{com}. sum neg_onset if dyad_startyr>1989 & neg_onset>1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}neg_onset {c |}{res}         32      4.5625     3.46352          2         16
{txt}
{com}. twoway histogram neg_onset if dyad_startyr!=1989, ///
>          discrete gap(10) ///
>          freq width(1) bcolor(navy) ///
>      ytitle("Frequency") ///
>          xtitle(" " "Number of Negotiations 1989-2009", size(small)) ///
>          xlabel(0(1)16, labsize(small)) ///
>      title("Count of Negotiations by Dyad", size(medlarge)) ///
>      graphregion(color(white)) ///
>          saving(FigureA1a.gph, replace) ///
>          legend(off)
{res}{txt}(file FigureA1a.gph saved)

{com}.          
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. qui logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)
{txt}
{com}. twoway hist t if neg_onset==1 & e(sample), ///
>          freq bcolor(navy) width(1) ///
>          discrete gap(10) ///
>      ytitle("Frequency") ///
>          xtitle(" " "Months Since Previous Negotatiations Ended", size(small)) ///
>      title("Time Between Negotiations", size(medlarge)) ///
>          xtick(0(6)60) ///
>          xlabel(0(6)60, labsize(small)) ///
>      graphregion(color(white))  ///
>          saving(FigureA1b.gph, replace) ///
>          legend(off)
{res}{txt}(file FigureA1b.gph saved)

{com}. 
. graph combine FigureA1a.gph FigureA1b.gph , ///
>   col(2) ///
>   title("Figure A1: Frequency Of and Time Between Negotiations", size(medlarge)) ///
>   subtitle("", size(medsmall)) ///
>   graphregion(color(white)) ///
>   saving(FigureA1.gph, replace) 
{res}{txt}(file FigureA1.gph saved)

{com}.   
. 
. *****************
. *****************
. *** Figure A2 ***
. *****************
. *****************
. * Number of negotiations by dyad-month
. collapse (sum) neg_onset (count) dyadid, by(dyad_months)
{txt}
{com}. gen prop_neg=neg_onset/dyadid
{txt}
{com}. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}dyad_months {c |}{res}        252       126.5    72.89033          1        252
{txt}{space 3}neg_onset {c |}{res}        252     .952381    1.578527          0         12
{txt}{space 6}dyadid {c |}{res}        252    16.55556    18.93375          2        104
{txt}{space 4}prop_neg {c |}{res}        252    .0446209    .0768224          0         .5
{txt}
{com}. list prop_neg if dyad_months==48
{txt}
     {c TLC}{hline 10}{c TRC}
     {c |} {res}prop_neg {txt}{c |}
     {c LT}{hline 10}{c RT}
 48. {c |} {res}.0384615 {txt}{c |}
     {c BLC}{hline 10}{c BRC}

{com}. 
. twoway line prop_neg dyad_months, ///
>      ytitle("Proportion of Dyads with Negotiations") ///
>          xtitle(" " "Dyad-Month") ///
>      title("Negotiations Per Dyad-Month", size(medlarge)) ///
>      graphregion(color(white)) ///
>          saving(FigureA2a.gph, replace) ///
>          legend(off)
{res}{txt}(file FigureA2a.gph saved)

{com}. 
. * Number of negotiations by year
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. collapse (sum) neg_onset, by(year)
{txt}
{com}. 
. twoway line neg_onset year, ///
>      ytitle("Number of Negotiations") ///
>          xtitle(" " "Year") ///
>      title("Negotiations Per Year", size(medlarge)) ///
>      graphregion(color(white)) ///
>          saving(FigureA2b.gph, replace) ///
>          legend(off)
{res}{txt}(file FigureA2b.gph saved)

{com}. 
. graph combine FigureA2a.gph FigureA2b.gph , ///
>   col(2) ///
>   title("Figure A2: Negotiations Over Time", size(medlarge)) ///
>   subtitle("", size(medsmall)) ///
>   graphregion(color(white)) ///
>   saving(FigureA2.gph, replace)  
{res}{txt}(file FigureA2.gph saved)

{com}. 
. 
. **********************************************
. **********************************************
. *** Figure A3: Imbalance Pre/Post Matching ***
. **********************************************
. **********************************************
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. 
. * Model 1
. matrix imbalance_pre = (0.00049, 1\0.10166, 2\0.06745, 3\0.15251, 4\0.19398, 5\0.21151, 6)
{txt}
{com}. matrix rownames imbalance_pre = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time"
{txt}
{com}. 
. gen graph_label="" if _n==1
{txt}(4,172 missing values generated)

{com}. replace graph_label="" if _n==2
{txt}(0 real changes made)

{com}. replace graph_label="" if _n==3
{txt}(0 real changes made)

{com}. replace graph_label="" if _n==4
{txt}(0 real changes made)

{com}. replace graph_label="" if _n==5
{txt}(0 real changes made)

{com}. replace graph_label="" if _n==6
{txt}(0 real changes made)

{com}. 
. _matplot imbalance_pre, ///
>         mlabel(graph_label) ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>         title("Model 1") ///
>     subtitle(" ", size(vsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///     
>         column(2 1) ///
>         yscale(reverse) ///
>     ylabel(1 "Concessions" 2 "Central Command" 3 "Parity" 4 "No. Rebel Groups" 5 "Conflict Duration" 6 "Time Since Neg.", angle(horizontal)) ///
>         fxsize(100) ///
>         saving (m1_imb_pre.gph, replace) 
{res}{txt}(file m1_imb_pre.gph saved)

{com}.                 
. matrix imbalance_post = (0.00000, 1\0.00000, 2\0.00000, 3\0.00000, 4\0.14594, 5\0.01865, 6)
{txt}
{com}. matrix rownames imbalance_post = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time Since Previous Negotiations"
{txt}
{com}. 
. _matplot imbalance_post, ///
>         mlabel(graph_label)  ///
>         xtitle(" " " ") ///
>     xlabel(0(.1).3) ///
>         ytitle(" ") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         column(2 1) ///
>         yscale(reverse) ///
>     ylabel(1 "Concessions" 2 "Central Command" 3 "Parity" 4 "No. Rebel Groups" 5 "Conflict Duration" 6 "Time Since Neg.", angle(horizontal)) ///
>         fxsize(100) ///
>         saving (m1_imb_post.gph, replace)       
{res}{txt}(file m1_imb_post.gph saved)

{com}.         
. * Model 2
. matrix imbalance_pre2 = (0.02284, 1\0.15692, 2\0.00974, 3\0.10371, 4\0.24825, 5\0.39332, 6)
{txt}
{com}. matrix rownames imbalance_pre2 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time"
{txt}
{com}. 
. _matplot imbalance_pre2, ///
>         mlabel(graph_label) ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).4) ///
>         ytitle(" ") ///
>         title("Model 2") ///
>     subtitle(" ", size(vsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///     
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m2_imb_pre.gph, replace) 
{res}{txt}(file m2_imb_pre.gph saved)

{com}.         
. matrix imbalance_post2 = (0.00000, 1\0.00000, 2\0.00000, 3\0.00000, 4\0.04854, 5\0.23194, 6)
{txt}
{com}. matrix rownames imbalance_post2 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time Since Previous Negotiations"
{txt}
{com}. 
. _matplot imbalance_post2, ///
>         mlabel(graph_label)  ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).4) ///
>         ytitle(" ") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m2_imb_post.gph, replace)
{res}{txt}(file m2_imb_post.gph saved)

{com}. 
. * Model 3
. matrix imbalance_pre3 = (0.02235, 1\0.025859, 2\0.07719, 3\0.21421, 4\0.12263, 5\0.27887, 6)
{txt}
{com}. matrix rownames imbalance_pre3 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time"
{txt}
{com}. 
. _matplot imbalance_pre3, ///
>         mlabel(graph_label) ///
>     xtitle(" " "Pre-Matching") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>         title("Model 3") ///
>     subtitle(" ", size(vsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///     
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m3_imb_pre.gph, replace) 
{res}{txt}(file m3_imb_pre.gph saved)

{com}.         
. matrix imbalance_post3 = (0.00000, 1\0.00000, 2\0.00000, 3\0.00000, 4\0.13073, 5\0.06944, 6)
{txt}
{com}. matrix rownames imbalance_post3 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time Since Previous Negotiations"
{txt}
{com}. 
. _matplot imbalance_post3, ///
>         mlabel(graph_label)  ///
>         xtitle(" " "Post-Matching") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m3_imb_post.gph, replace)       
{res}{txt}(file m3_imb_post.gph saved)

{com}.         
. * Model 4
. matrix imbalance_pre4 = (0.08933, 1\0.22998, 2\0.11128, 3\0.14443, 4\0.18139, 5\0.13748, 6)
{txt}
{com}. matrix rownames imbalance_pre4 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time"
{txt}
{com}. 
. _matplot imbalance_pre4, ///
>         mlabel(graph_label) ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>         title("Model 4") ///
>     subtitle(" ", size(vsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///     
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m4_imb_pre.gph, replace) 
{res}{txt}(file m4_imb_pre.gph saved)

{com}.         
. matrix imbalance_post4 = (0.00000, 1\0, 2\0, 3\0.00000, 4\0.2073, 5\0.0000, 6)
{txt}
{com}. matrix rownames imbalance_post4 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time Since Previous Negotiations"
{txt}
{com}. 
. _matplot imbalance_post4, ///
>         mlabel(graph_label)  ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m4_imb_post.gph, replace)       
{res}{txt}(file m4_imb_post.gph saved)

{com}.         
. * Model 5
. matrix imbalance_pre5 = (0.02131, 1\0.17237, 2\0.05566, 3\0.21307, 4\0.22437, 5\0.16983, 6)
{txt}
{com}. matrix rownames imbalance_pre5 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time"
{txt}
{com}. 
. _matplot imbalance_pre5, ///
>         mlabel(graph_label) ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>         title("Model 5") ///
>     subtitle(" ", size(vsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///     
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m5_imb_pre.gph, replace) 
{res}{txt}(file m5_imb_pre.gph saved)

{com}.         
. matrix imbalance_post5 = (0.00000, 1\0, 2\0, 3\0.00000, 4\0.08984, 5\0.02865, 6)
{txt}
{com}. matrix rownames imbalance_post5 = "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration" "Time Since Previous Negotiations"
{txt}
{com}. 
. _matplot imbalance_post5, ///
>         mlabel(graph_label)  ///
>         xtitle(" " " ") ///
>         xlabel(0(.1).3) ///
>         ytitle(" ") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         column(2 1) ///
>         yscale(reverse) ///
>         ylabel(1 " " 2 " " 3 " " 4 " " 5 " " 6 " ") ///
>         fxsize(70) ///
>         saving (m5_imb_post.gph, replace)
{res}{txt}(file m5_imb_post.gph saved)

{com}.         
. graph combine m1_imb_pre.gph m2_imb_pre.gph m3_imb_pre.gph m4_imb_pre.gph m5_imb_pre.gph m1_imb_post.gph m2_imb_post.gph m3_imb_post.gph m4_imb_post.gph m5_imb_post.gph, col(5) ///
>   title("Figure A3: Univariate Imbalance Pre- & Post-Matching, Models in Table 3", size(med)) ///
>   subtitle(" ", size(small)) ///
>   graphregion(color(white)) ///
>   imargin(0 0 0 0) ///  
>   saving(figureA3.gph, replace)         
{res}{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(file figureA3.gph saved)

{com}.         
.         
. *****************************************
. *****************************************
. *** Figure A4: Predictive Performance ***
. *****************************************
. *****************************************
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. 
. *Model 1: full model AUC=0.7521
. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.818
{txt}2-fold (N=148).........AUC ={res}  0.738
{txt}3-fold (N=149).........AUC ={res}  0.734
{txt}4-fold (N=148).........AUC ={res}  0.781
{txt}5-fold (N=148).........AUC ={res}  0.632
{txt}6-fold (N=149).........AUC ={res}  0.721
{txt}7-fold (N=148).........AUC ={res}  0.738
{txt}8-fold (N=149).........AUC ={res}  0.748
{txt}9-fold (N=148).........AUC ={res}  0.842
{txt}10-fold (N=148).........AUC ={res}  0.769

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7521
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7062, 0.7848
{txt}cvSD AUC:                        {c |}{res} 0.0574
{txt}{hline 64}

{com}. *omitting battes_escalation AUC=0.7462
. cvauroc neg_DV weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=158).........AUC ={res}  0.794
{txt}2-fold (N=158).........AUC ={res}  0.642
{txt}3-fold (N=157).........AUC ={res}  0.656
{txt}4-fold (N=158).........AUC ={res}  0.859
{txt}5-fold (N=157).........AUC ={res}  0.691
{txt}6-fold (N=158).........AUC ={res}  0.771
{txt}7-fold (N=158).........AUC ={res}  0.755
{txt}8-fold (N=157).........AUC ={res}  0.692
{txt}9-fold (N=158).........AUC ={res}  0.758
{txt}10-fold (N=157).........AUC ={res}  0.842

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7462
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.6839, 0.7622
{txt}cvSD AUC:                        {c |}{res} 0.0744
{txt}{hline 64}

{com}. *omitting weak_con_last_neg_dum AUC=0.7417
. cvauroc neg_DV battles_escalation2 battles_escalation3 str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.783
{txt}2-fold (N=148).........AUC ={res}  0.752
{txt}3-fold (N=149).........AUC ={res}  0.657
{txt}4-fold (N=148).........AUC ={res}  0.780
{txt}5-fold (N=148).........AUC ={res}  0.758
{txt}6-fold (N=149).........AUC ={res}  0.766
{txt}7-fold (N=148).........AUC ={res}  0.724
{txt}8-fold (N=149).........AUC ={res}  0.676
{txt}9-fold (N=148).........AUC ={res}  0.743
{txt}10-fold (N=148).........AUC ={res}  0.777

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7417
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.6915, 0.7711
{txt}cvSD AUC:                        {c |}{res} 0.0436
{txt}{hline 64}

{com}. *omitting str_centcomm AUC=0.7521
. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.818
{txt}2-fold (N=148).........AUC ={res}  0.738
{txt}3-fold (N=149).........AUC ={res}  0.734
{txt}4-fold (N=148).........AUC ={res}  0.781
{txt}5-fold (N=148).........AUC ={res}  0.632
{txt}6-fold (N=149).........AUC ={res}  0.721
{txt}7-fold (N=148).........AUC ={res}  0.738
{txt}8-fold (N=149).........AUC ={res}  0.748
{txt}9-fold (N=148).........AUC ={res}  0.842
{txt}10-fold (N=148).........AUC ={res}  0.769

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7521
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7062, 0.7848
{txt}cvSD AUC:                        {c |}{res} 0.0574
{txt}{hline 64}

{com}. *omitting parity AUC=0.7421
. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.784
{txt}2-fold (N=148).........AUC ={res}  0.760
{txt}3-fold (N=149).........AUC ={res}  0.677
{txt}4-fold (N=148).........AUC ={res}  0.831
{txt}5-fold (N=148).........AUC ={res}  0.504
{txt}6-fold (N=149).........AUC ={res}  0.779
{txt}7-fold (N=148).........AUC ={res}  0.772
{txt}8-fold (N=149).........AUC ={res}  0.725
{txt}9-fold (N=148).........AUC ={res}  0.788
{txt}10-fold (N=148).........AUC ={res}  0.801

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7421
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.6994, 0.7788
{txt}cvSD AUC:                        {c |}{res} 0.0936
{txt}{hline 64}

{com}. *omitting groupnum AUC=0.7523
. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.863
{txt}2-fold (N=148).........AUC ={res}  0.654
{txt}3-fold (N=149).........AUC ={res}  0.742
{txt}4-fold (N=148).........AUC ={res}  0.762
{txt}5-fold (N=148).........AUC ={res}  0.697
{txt}6-fold (N=149).........AUC ={res}  0.777
{txt}7-fold (N=148).........AUC ={res}  0.785
{txt}8-fold (N=149).........AUC ={res}  0.762
{txt}9-fold (N=148).........AUC ={res}  0.763
{txt}10-fold (N=148).........AUC ={res}  0.719

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7523
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7049, 0.7835
{txt}cvSD AUC:                        {c |}{res} 0.0557
{txt}{hline 64}

{com}. *omitting dyad_months AUC=0.7564 
. cvauroc neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity groupnum t t2 t3, fit seed(11235)
{txt}1-fold (N=149).........AUC ={res}  0.818
{txt}2-fold (N=148).........AUC ={res}  0.716
{txt}3-fold (N=149).........AUC ={res}  0.779
{txt}4-fold (N=148).........AUC ={res}  0.831
{txt}5-fold (N=148).........AUC ={res}  0.593
{txt}6-fold (N=149).........AUC ={res}  0.745
{txt}7-fold (N=148).........AUC ={res}  0.704
{txt}8-fold (N=149).........AUC ={res}  0.741
{txt}9-fold (N=148).........AUC ={res}  0.859
{txt}10-fold (N=148).........AUC ={res}  0.778

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7564
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7032, 0.7813
{txt}cvSD AUC:                        {c |}{res} 0.0761
{txt}{hline 64}

{com}. 
. *Model 2: full model AUC=0.7767
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.721
{txt}2-fold (N=118).........AUC ={res}  0.854
{txt}3-fold (N=118).........AUC ={res}  0.744
{txt}4-fold (N=118).........AUC ={res}  0.660
{txt}5-fold (N=118).........AUC ={res}  0.794
{txt}6-fold (N=118).........AUC ={res}  0.815
{txt}7-fold (N=118).........AUC ={res}  0.808
{txt}8-fold (N=118).........AUC ={res}  0.820
{txt}9-fold (N=118).........AUC ={res}  0.775
{txt}10-fold (N=117).........AUC ={res}  0.774

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7767
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7352, 0.8141
{txt}cvSD AUC:                        {c |}{res} 0.0561
{txt}{hline 64}

{com}. *omitting battles_escalation##i.thirdparty_involvement AUC=0.7462
. cvauroc neg_DV weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=158).........AUC ={res}  0.794
{txt}2-fold (N=158).........AUC ={res}  0.642
{txt}3-fold (N=157).........AUC ={res}  0.656
{txt}4-fold (N=158).........AUC ={res}  0.859
{txt}5-fold (N=157).........AUC ={res}  0.691
{txt}6-fold (N=158).........AUC ={res}  0.771
{txt}7-fold (N=158).........AUC ={res}  0.755
{txt}8-fold (N=157).........AUC ={res}  0.692
{txt}9-fold (N=158).........AUC ={res}  0.758
{txt}10-fold (N=157).........AUC ={res}  0.842

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7462
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.6839, 0.7622
{txt}cvSD AUC:                        {c |}{res} 0.0744
{txt}{hline 64}

{com}. *omitting weak_con_last_neg_dum AUC=0.7732
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re str_centcomm parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.777
{txt}2-fold (N=118).........AUC ={res}  0.876
{txt}3-fold (N=118).........AUC ={res}  0.678
{txt}4-fold (N=118).........AUC ={res}  0.688
{txt}5-fold (N=118).........AUC ={res}  0.842
{txt}6-fold (N=118).........AUC ={res}  0.830
{txt}7-fold (N=118).........AUC ={res}  0.770
{txt}8-fold (N=118).........AUC ={res}  0.788
{txt}9-fold (N=118).........AUC ={res}  0.718
{txt}10-fold (N=117).........AUC ={res}  0.765

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7732
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7373, 0.8158
{txt}cvSD AUC:                        {c |}{res} 0.0650
{txt}{hline 64}

{com}. *omitting str_centcomm AUC=0.7870
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum parity groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=125).........AUC ={res}  0.772
{txt}2-fold (N=124).........AUC ={res}  0.761
{txt}3-fold (N=124).........AUC ={res}  0.680
{txt}4-fold (N=124).........AUC ={res}  0.790
{txt}5-fold (N=124).........AUC ={res}  0.882
{txt}6-fold (N=125).........AUC ={res}  0.866
{txt}7-fold (N=124).........AUC ={res}  0.812
{txt}8-fold (N=124).........AUC ={res}  0.813
{txt}9-fold (N=124).........AUC ={res}  0.726
{txt}10-fold (N=124).........AUC ={res}  0.769

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7870
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7481, 0.8224
{txt}cvSD AUC:                        {c |}{res} 0.0605
{txt}{hline 64}

{com}. *omitting parity AUC=0.7767
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm groupnum dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.783
{txt}2-fold (N=118).........AUC ={res}  0.871
{txt}3-fold (N=118).........AUC ={res}  0.691
{txt}4-fold (N=118).........AUC ={res}  0.598
{txt}5-fold (N=118).........AUC ={res}  0.816
{txt}6-fold (N=118).........AUC ={res}  0.820
{txt}7-fold (N=118).........AUC ={res}  0.811
{txt}8-fold (N=118).........AUC ={res}  0.863
{txt}9-fold (N=118).........AUC ={res}  0.773
{txt}10-fold (N=117).........AUC ={res}  0.740

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7767
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7384, 0.8179
{txt}cvSD AUC:                        {c |}{res} 0.0827
{txt}{hline 64}

{com}. *omitting groupnum AUC=0.7748
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity dyad_months t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.750
{txt}2-fold (N=118).........AUC ={res}  0.834
{txt}3-fold (N=118).........AUC ={res}  0.740
{txt}4-fold (N=118).........AUC ={res}  0.652
{txt}5-fold (N=118).........AUC ={res}  0.760
{txt}6-fold (N=118).........AUC ={res}  0.820
{txt}7-fold (N=118).........AUC ={res}  0.851
{txt}8-fold (N=118).........AUC ={res}  0.803
{txt}9-fold (N=118).........AUC ={res}  0.789
{txt}10-fold (N=117).........AUC ={res}  0.751

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7748
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7364, 0.8150
{txt}cvSD AUC:                        {c |}{res} 0.0579
{txt}{hline 64}

{com}. *omitting dyad_months AUC=0.7813
. cvauroc neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity groupnum t t2 t3, fit seed(11235)
{txt}1-fold (N=118).........AUC ={res}  0.737
{txt}2-fold (N=118).........AUC ={res}  0.855
{txt}3-fold (N=118).........AUC ={res}  0.734
{txt}4-fold (N=118).........AUC ={res}  0.640
{txt}5-fold (N=118).........AUC ={res}  0.801
{txt}6-fold (N=118).........AUC ={res}  0.814
{txt}7-fold (N=118).........AUC ={res}  0.851
{txt}8-fold (N=118).........AUC ={res}  0.792
{txt}9-fold (N=118).........AUC ={res}  0.772
{txt}10-fold (N=117).........AUC ={res}  0.818

{txt}Model:{res}logistic

{txt}Seed:{res}11235

{txt}{hline 68}
Cross-validated (cv) mean AUC, SD and Bootstrap Bias Corrected 95%CI
{hline 68}
cvMean AUC:                      {c |}{res} 0.7813
{txt}Bootstrap bias corrected 95%CI:  {c |}{res} 0.7345, 0.8148
{txt}cvSD AUC:                        {c |}{res} 0.0645
{txt}{hline 64}

{com}. 
. * Figure A4: AUC Omitting Each Variable, Based on Models 1 and 2 Table 2 *
. matrix aucs_m1 = (0.7462, 1\0.7417, 1\0.7521, 1\0.7421, 1\0.7523, 1\0.7564, 1)
{txt}
{com}. matrix rownames aucs_m1 = "(De-)Escalation" "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration"
{txt}
{com}. 
. gen graph_label="Post-Negotiation (De-)Escalation" if _n==1
{txt}(4,171 missing values generated)

{com}. replace graph_label="Prior Concessions" if _n==2
{txt}(1 real change made)

{com}. replace graph_label="Strength of Central Command" if _n==3
{txt}(1 real change made)

{com}. replace graph_label="Parity" if _n==4
{txt}(1 real change made)

{com}. replace graph_label="Number of Rebel Groups" if _n==5
{txt}(1 real change made)

{com}. replace graph_label="Conflict Duration (Months)" if _n==6
{txt}(1 real change made)

{com}. 
. gen position=12
{txt}
{com}. replace position=6 if _n==3 | _n==2
{txt}(2 real changes made)

{com}. 
. _matplot aucs_m1, yline(0.7521, ///
>         lw(thin)) mlabel(graph_label) mlabv(position) mlabsize(medsmall) mlabcolor(black) ///
>         yscale(range(0.74, 0.79)) ///
>         ymtick(0.74 (.01) 0.79) ///
>         ylabel(0.74 (.01) 0.79) ///
>         xlabel(none) ///
>     ytitle("Predictive Power (AUC)" " ") ///
>     xtitle(" ") ///
>     title("Model 1") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         saving (m1_auc.gph, replace) 
{res}{txt}(note: file m1_auc.gph not found)
{res}{txt}(file m1_auc.gph saved)

{com}.         
. matrix aucs_m2 = (0.7462, 1\0.7732, 1\0.7870, 1\0.7767, 1\0.7748, 1\0.7813, 1)
{txt}
{com}. matrix rownames aucs_m2 = "(De-)Escalation*ThirdParty_Involvement" "Concessions" "Central_Command" "Parity" "Number_Rebel_Groups" "Conflict_Duration"
{txt}
{com}. 
. gen graph_label2="Post-Negotiation (De-)Escalation*Third-Party" if _n==1
{txt}(4,171 missing values generated)

{com}. replace graph_label2="Prior Concessions" if _n==2
{txt}(1 real change made)

{com}. replace graph_label2="Strength of Central Command" if _n==3
{txt}(1 real change made)

{com}. replace graph_label2="Parity" if _n==4
{txt}(1 real change made)

{com}. replace graph_label2="Number of Rebel Groups" if _n==5
{txt}(1 real change made)

{com}. replace graph_label2="Conflict Duration (Months)" if _n==6
{txt}(1 real change made)

{com}. 
. gen position2=12
{txt}
{com}. replace position2=6 if _n==2
{txt}(1 real change made)

{com}. replace position2=3 if _n==5
{txt}(1 real change made)

{com}. 
. _matplot aucs_m2, yline(0.7767, ///
>         lw(thin)) mlabel(graph_label2) mlabv(position2) mlabsize(medsmall) mlabcolor(black) ///
>         yscale(range(0.74, 0.79)) ///
>         ymtick(0.74 (.01) 0.79) ///
>         ylabel(0.74 (.01) 0.79) ///
>         xlabel(none) ///
>     ytitle(" ") ///
>     xtitle(" ") ///
>     title("Model 2") ///
>     subtitle(" ", size(medsmall)) ///
>     graphregion(color(white)) ///
>     legend(off) ///
>         saving (m2_auc.gph, replace)   
{res}{txt}(note: file m2_auc.gph not found)
{res}{txt}(file m2_auc.gph saved)

{com}. 
. graph combine m1_auc.gph m2_auc.gph, col(2) ///
>   title("Figure A4: K-Fold Cross Validation", size(med)) ///
>   subtitle("", size(medsmall)) ///
>   graphregion(color(white)) ///
>   note("Note: Figure depicts the AUC when omitting each variable.", size(small)) ///
>   saving(figureA4.gph, replace)         
{res}{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(note: file figureA4.gph not found)
{res}{txt}(file figureA4.gph saved)

{com}. 
. drop _fit graph_label position graph_label2 position2
{txt}
{com}. 
.   
.   
. ***********************************************************************
. ***********************************************************************
. *** Table A1: Alternative Post-Negotiation (De-)Escalation Measures ***
. ***********************************************************************
. ***********************************************************************
.         
. * Model 1 (Continuous Battle Onsets Escalation) 
. logit neg_DV battles_escalation_continuous weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-468.20555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-420.50468}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.97584}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.02395}  
Iteration 4:{space 3}log pseudolikelihood = {res:-408.46719}  
Iteration 5:{space 3}log pseudolikelihood = {res:-407.57296}  
Iteration 6:{space 3}log pseudolikelihood = {res:  -406.523}  
Iteration 7:{space 3}log pseudolikelihood = {res:-406.07108}  
Iteration 8:{space 3}log pseudolikelihood = {res: -406.0114}  
Iteration 9:{space 3}log pseudolikelihood = {res:-406.01133}  
Iteration 10:{space 2}log pseudolikelihood = {res:-406.01133}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    111.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-406.01133{txt}{col 49}Pseudo R2{col 67}= {res}    0.1328

{txt}{ralign 92:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation_conti~s {c |}{col 28}{res}{space 2}-.0387537{col 40}{space 2} .0166083{col 51}{space 1}   -2.33{col 60}{space 3}0.020{col 68}{space 4}-.0713054{col 81}{space 3}-.0062021
{txt}{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4664787{col 40}{space 2} .1621204{col 51}{space 1}    2.88{col 60}{space 3}0.004{col 68}{space 4} .1487285{col 81}{space 3} .7842289
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2} .0058192{col 40}{space 2} .1134903{col 51}{space 1}    0.05{col 60}{space 3}0.959{col 68}{space 4}-.2166177{col 81}{space 3} .2282561
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .4352028{col 40}{space 2} .1769942{col 51}{space 1}    2.46{col 60}{space 3}0.014{col 68}{space 4} .0883006{col 81}{space 3}  .782105
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2} .0301712{col 40}{space 2} .1484187{col 51}{space 1}    0.20{col 60}{space 3}0.839{col 68}{space 4}-.2607241{col 81}{space 3} .3210666
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0014388{col 40}{space 2} .0024714{col 51}{space 1}   -0.58{col 60}{space 3}0.560{col 68}{space 4}-.0062826{col 81}{space 3} .0034051
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1946389{col 40}{space 2} .0479648{col 51}{space 1}   -4.06{col 60}{space 3}0.000{col 68}{space 4}-.2886482{col 81}{space 3}-.1006295
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0057459{col 40}{space 2} .0023944{col 51}{space 1}    2.40{col 60}{space 3}0.016{col 68}{space 4}  .001053{col 81}{space 3} .0104388
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2} -.000057{col 40}{space 2} .0000287{col 51}{space 1}   -1.99{col 60}{space 3}0.047{col 68}{space 4}-.0001131{col 81}{space 3}-7.87e-07
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-1.052336{col 40}{space 2} .4944523{col 51}{space 1}   -2.13{col 60}{space 3}0.033{col 68}{space 4}-2.021445{col 81}{space 3}-.0832275
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 111 failures and 0 successes completely determined.{p_end}

{com}. estimates store a1
{txt}
{com}. 
. * Model 2 (Continuous Battle Onsets Escalation & Third-Party Involvement) 
. logit neg_DV c.battles_escalation_continuous##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-319.45608}  
Iteration 2:{space 3}log pseudolikelihood = {res:-305.26904}  
Iteration 3:{space 3}log pseudolikelihood = {res:-304.26213}  
Iteration 4:{space 3}log pseudolikelihood = {res:-303.64009}  
Iteration 5:{space 3}log pseudolikelihood = {res:-302.65041}  
Iteration 6:{space 3}log pseudolikelihood = {res: -301.9655}  
Iteration 7:{space 3}log pseudolikelihood = {res:-301.75035}  
Iteration 8:{space 3}log pseudolikelihood = {res:-301.74112}  
Iteration 9:{space 3}log pseudolikelihood = {res:-301.74108}  
Iteration 10:{space 2}log pseudolikelihood = {res:-301.74108}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    176.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-301.74108{txt}{col 49}Pseudo R2{col 67}= {res}    0.1798

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation_conti~s {c |}{col 28}{res}{space 2}-.1732341{col 40}{space 2}  .040167{col 51}{space 1}   -4.31{col 60}{space 3}0.000{col 68}{space 4}  -.25196{col 81}{space 3}-.0945082
{txt}{space 2}1.thirdparty_involvement {c |}{col 28}{res}{space 2} 1.133342{col 40}{space 2} .3425991{col 51}{space 1}    3.31{col 60}{space 3}0.001{col 68}{space 4} .4618599{col 81}{space 3} 1.804824
{txt}{space 26} {c |}
{space 4}thirdparty_involvement#{c |}
{space 24}c. {c |}
battles_escalation_conti~s {c |}
{space 24}1  {c |}{col 28}{res}{space 2} .2146849{col 40}{space 2} .0505512{col 51}{space 1}    4.25{col 60}{space 3}0.000{col 68}{space 4} .1156063{col 81}{space 3} .3137635
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4313656{col 40}{space 2} .1948382{col 51}{space 1}    2.21{col 60}{space 3}0.027{col 68}{space 4} .0494897{col 81}{space 3} .8132414
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.1523811{col 40}{space 2} .0844911{col 51}{space 1}   -1.80{col 60}{space 3}0.071{col 68}{space 4}-.3179807{col 81}{space 3} .0132185
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .5079833{col 40}{space 2}  .213881{col 51}{space 1}    2.38{col 60}{space 3}0.018{col 68}{space 4} .0887843{col 81}{space 3} .9271824
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}-.1168568{col 40}{space 2} .1569704{col 51}{space 1}   -0.74{col 60}{space 3}0.457{col 68}{space 4}-.4245131{col 81}{space 3} .1907995
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0003934{col 40}{space 2} .0019001{col 51}{space 1}   -0.21{col 60}{space 3}0.836{col 68}{space 4}-.0041175{col 81}{space 3} .0033308
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1815804{col 40}{space 2} .0537003{col 51}{space 1}   -3.38{col 60}{space 3}0.001{col 68}{space 4}-.2868309{col 81}{space 3}-.0763298
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0056868{col 40}{space 2} .0025007{col 51}{space 1}    2.27{col 60}{space 3}0.023{col 68}{space 4} .0007855{col 81}{space 3}  .010588
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000552{col 40}{space 2}  .000028{col 51}{space 1}   -1.97{col 60}{space 3}0.049{col 68}{space 4}-.0001102{col 81}{space 3}-2.15e-07
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-1.348176{col 40}{space 2} .4571808{col 51}{space 1}   -2.95{col 60}{space 3}0.003{col 68}{space 4}-2.244234{col 81}{space 3}-.4521185
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a2
{txt}
{com}. 
. * Model 3 (Categorical Battle Deaths) 
. logit neg_DV ib0.deaths_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-468.20555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-420.05035}  
Iteration 2:{space 3}log pseudolikelihood = {res:-409.45473}  
Iteration 3:{space 3}log pseudolikelihood = {res:-408.50627}  
Iteration 4:{space 3}log pseudolikelihood = {res:-407.93867}  
Iteration 5:{space 3}log pseudolikelihood = {res:-407.04338}  
Iteration 6:{space 3}log pseudolikelihood = {res:-405.97397}  
Iteration 7:{space 3}log pseudolikelihood = {res:-405.48553}  
Iteration 8:{space 3}log pseudolikelihood = {res:-405.42473}  
Iteration 9:{space 3}log pseudolikelihood = {res:-405.42462}  
Iteration 10:{space 2}log pseudolikelihood = {res:-405.42462}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    116.43
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-405.42462{txt}{col 49}Pseudo R2{col 67}= {res}    0.1341

{txt}{ralign 87:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}deaths_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.3789935{col 35}{space 2} .2399102{col 46}{space 1}   -1.58{col 55}{space 3}0.114{col 63}{space 4}-.8492088{col 76}{space 3} .0912218
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.4175578{col 35}{space 2} .2329103{col 46}{space 1}   -1.79{col 55}{space 3}0.073{col 63}{space 4}-.8740537{col 76}{space 3} .0389381
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2}  .446055{col 35}{space 2} .1729102{col 46}{space 1}    2.58{col 55}{space 3}0.010{col 63}{space 4} .1071572{col 76}{space 3} .7849528
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0126763{col 35}{space 2} .1137524{col 46}{space 1}   -0.11{col 55}{space 3}0.911{col 63}{space 4} -.235627{col 76}{space 3} .2102743
{txt}{space 15}parity {c |}{col 23}{res}{space 2}  .533739{col 35}{space 2} .1870575{col 46}{space 1}    2.85{col 55}{space 3}0.004{col 63}{space 4} .1671131{col 76}{space 3}  .900365
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0412997{col 35}{space 2} .1391384{col 46}{space 1}    0.30{col 55}{space 3}0.767{col 63}{space 4}-.2314065{col 76}{space 3} .3140059
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0019232{col 35}{space 2} .0023947{col 46}{space 1}   -0.80{col 55}{space 3}0.422{col 63}{space 4}-.0066168{col 76}{space 3} .0027703
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1963031{col 35}{space 2} .0483947{col 46}{space 1}   -4.06{col 55}{space 3}0.000{col 63}{space 4}-.2911549{col 76}{space 3}-.1014513
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0058977{col 35}{space 2} .0024256{col 46}{space 1}    2.43{col 55}{space 3}0.015{col 63}{space 4} .0011436{col 76}{space 3} .0106518
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000588{col 35}{space 2} .0000292{col 46}{space 1}   -2.01{col 55}{space 3}0.044{col 63}{space 4}-.0001159{col 76}{space 3}-1.57e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.7907742{col 35}{space 2} .4696105{col 46}{space 1}   -1.68{col 55}{space 3}0.092{col 63}{space 4}-1.711194{col 76}{space 3} .1296455
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 112 failures and 0 successes completely determined.{p_end}

{com}. estimates store a3
{txt}
{com}. 
. * Model 4 (Categorical Battle Deaths & Third-Party Involvement)
. logit neg_DV ib0.deaths_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-321.97629}  
Iteration 2:{space 3}log pseudolikelihood = {res: -309.2477}  
Iteration 3:{space 3}log pseudolikelihood = {res:-308.27041}  
Iteration 4:{space 3}log pseudolikelihood = {res: -307.5931}  
Iteration 5:{space 3}log pseudolikelihood = {res:-306.64945}  
Iteration 6:{space 3}log pseudolikelihood = {res:-305.87447}  
Iteration 7:{space 3}log pseudolikelihood = {res:-305.66527}  
Iteration 8:{space 3}log pseudolikelihood = {res:-305.65623}  
Iteration 9:{space 3}log pseudolikelihood = {res:-305.65621}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    196.71
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-305.65621{txt}{col 49}Pseudo R2{col 67}= {res}    0.1691

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}deaths_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.1918943{col 38}{space 2} .4999512{col 49}{space 1}   -0.38{col 58}{space 3}0.701{col 66}{space 4}-1.171781{col 79}{space 3} .7879919
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.6538611{col 38}{space 2} .4333644{col 49}{space 1}   -1.51{col 58}{space 3}0.131{col 66}{space 4} -1.50324{col 79}{space 3} .1955175
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2}  .900163{col 38}{space 2} .3965364{col 49}{space 1}    2.27{col 58}{space 3}0.023{col 66}{space 4} .1229659{col 79}{space 3}  1.67736
{txt}{space 24} {c |}
{space 7}deaths_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .1576061{col 38}{space 2} .6006794{col 49}{space 1}    0.26{col 58}{space 3}0.793{col 66}{space 4}-1.019704{col 79}{space 3} 1.334916
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2}  .658765{col 38}{space 2} .4882771{col 49}{space 1}    1.35{col 58}{space 3}0.177{col 66}{space 4}-.2982405{col 79}{space 3}  1.61577
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4842716{col 38}{space 2} .2178554{col 49}{space 1}    2.22{col 58}{space 3}0.026{col 66}{space 4} .0572828{col 79}{space 3} .9112603
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.1096718{col 38}{space 2} .0979313{col 49}{space 1}   -1.12{col 58}{space 3}0.263{col 66}{space 4}-.3016138{col 79}{space 3} .0822701
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .4629527{col 38}{space 2} .2151277{col 49}{space 1}    2.15{col 58}{space 3}0.031{col 66}{space 4} .0413103{col 79}{space 3} .8845952
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0920805{col 38}{space 2} .1722958{col 49}{space 1}   -0.53{col 58}{space 3}0.593{col 66}{space 4}-.4297741{col 79}{space 3}  .245613
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0010216{col 38}{space 2} .0022409{col 49}{space 1}   -0.46{col 58}{space 3}0.648{col 66}{space 4}-.0054137{col 79}{space 3} .0033706
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1858769{col 38}{space 2} .0542468{col 49}{space 1}   -3.43{col 58}{space 3}0.001{col 66}{space 4}-.2921987{col 79}{space 3}-.0795552
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0058467{col 38}{space 2} .0025345{col 49}{space 1}    2.31{col 58}{space 3}0.021{col 66}{space 4} .0008792{col 79}{space 3} .0108142
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000567{col 38}{space 2} .0000286{col 49}{space 1}   -1.98{col 58}{space 3}0.047{col 66}{space 4}-.0001127{col 79}{space 3}-6.81e-07
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.200004{col 38}{space 2} .4854248{col 49}{space 1}   -2.47{col 58}{space 3}0.013{col 66}{space 4}-2.151419{col 79}{space 3}-.2485885
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 83 failures and 0 successes completely determined.{p_end}

{com}. estimates store a4
{txt}
{com}.         
. * Model 5 (Continuous Battle Deaths Escalation)
. logit neg_DV deaths_escalation_continuousln weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-468.20555}  
Iteration 1:{space 3}log pseudolikelihood = {res:-420.29943}  
Iteration 2:{space 3}log pseudolikelihood = {res:-410.02337}  
Iteration 3:{space 3}log pseudolikelihood = {res:-409.08139}  
Iteration 4:{space 3}log pseudolikelihood = {res:-408.51556}  
Iteration 5:{space 3}log pseudolikelihood = {res:-407.64714}  
Iteration 6:{space 3}log pseudolikelihood = {res:-406.62161}  
Iteration 7:{space 3}log pseudolikelihood = {res:-406.16392}  
Iteration 8:{space 3}log pseudolikelihood = {res:-406.10897}  
Iteration 9:{space 3}log pseudolikelihood = {res:-406.10889}  
Iteration 10:{space 2}log pseudolikelihood = {res:-406.10889}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    107.51
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-406.10889{txt}{col 49}Pseudo R2{col 67}= {res}    0.1326

{txt}{ralign 92:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
deaths_escalation_contin~n {c |}{col 28}{res}{space 2}-.0511427{col 40}{space 2} .0244672{col 51}{space 1}   -2.09{col 60}{space 3}0.037{col 68}{space 4}-.0990976{col 81}{space 3}-.0031877
{txt}{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4720372{col 40}{space 2} .1680068{col 51}{space 1}    2.81{col 60}{space 3}0.005{col 68}{space 4}   .14275{col 81}{space 3} .8013244
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2} .0071982{col 40}{space 2} .1160269{col 51}{space 1}    0.06{col 60}{space 3}0.951{col 68}{space 4}-.2202104{col 81}{space 3} .2346067
{txt}{space 20}parity {c |}{col 28}{res}{space 2}  .465421{col 40}{space 2} .1846236{col 51}{space 1}    2.52{col 60}{space 3}0.012{col 68}{space 4} .1035655{col 81}{space 3} .8272766
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2} .0330909{col 40}{space 2} .1464497{col 51}{space 1}    0.23{col 60}{space 3}0.821{col 68}{space 4}-.2539452{col 81}{space 3}  .320127
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0019766{col 40}{space 2} .0023958{col 51}{space 1}   -0.83{col 60}{space 3}0.409{col 68}{space 4}-.0066723{col 81}{space 3} .0027191
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1950643{col 40}{space 2} .0480036{col 51}{space 1}   -4.06{col 60}{space 3}0.000{col 68}{space 4}-.2891497{col 81}{space 3} -.100979
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0057688{col 40}{space 2} .0024219{col 51}{space 1}    2.38{col 60}{space 3}0.017{col 68}{space 4} .0010219{col 81}{space 3} .0105156
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000572{col 40}{space 2} .0000292{col 51}{space 1}   -1.96{col 60}{space 3}0.050{col 68}{space 4}-.0001144{col 81}{space 3}-3.08e-08
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-1.056299{col 40}{space 2} .4995657{col 51}{space 1}   -2.11{col 60}{space 3}0.034{col 68}{space 4} -2.03543{col 81}{space 3}-.0771678
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 112 failures and 0 successes completely determined.{p_end}

{com}. estimates store a5
{txt}
{com}. 
. * Model 6 (Continuous Battle Deaths Escalation & Third-Party Involvement)
. logit neg_DV c.deaths_escalation_continuousln##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-321.82333}  
Iteration 2:{space 3}log pseudolikelihood = {res:-309.18567}  
Iteration 3:{space 3}log pseudolikelihood = {res:-308.21506}  
Iteration 4:{space 3}log pseudolikelihood = {res:-307.55978}  
Iteration 5:{space 3}log pseudolikelihood = {res:-306.67206}  
Iteration 6:{space 3}log pseudolikelihood = {res:-305.92748}  
Iteration 7:{space 3}log pseudolikelihood = {res:-305.72759}  
Iteration 8:{space 3}log pseudolikelihood = {res:-305.71949}  
Iteration 9:{space 3}log pseudolikelihood = {res:-305.71947}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}    222.63
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-305.71947{txt}{col 49}Pseudo R2{col 67}= {res}    0.1690

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
deaths_escalation_contin~n {c |}{col 28}{res}{space 2}-.1065742{col 40}{space 2} .0667576{col 51}{space 1}   -1.60{col 60}{space 3}0.110{col 68}{space 4}-.2374166{col 81}{space 3} .0242682
{txt}{space 2}1.thirdparty_involvement {c |}{col 28}{res}{space 2} 1.158547{col 40}{space 2} .4039821{col 51}{space 1}    2.87{col 60}{space 3}0.004{col 68}{space 4} .3667562{col 81}{space 3} 1.950337
{txt}{space 26} {c |}
{space 4}thirdparty_involvement#{c |}
{space 24}c. {c |}
deaths_escalation_contin~n {c |}
{space 24}1  {c |}{col 28}{res}{space 2} .1209241{col 40}{space 2} .0696519{col 51}{space 1}    1.74{col 60}{space 3}0.083{col 68}{space 4}-.0155912{col 81}{space 3} .2574394
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4975195{col 40}{space 2} .2136382{col 51}{space 1}    2.33{col 60}{space 3}0.020{col 68}{space 4} .0787963{col 81}{space 3} .9162428
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.1196038{col 40}{space 2} .0894581{col 51}{space 1}   -1.34{col 60}{space 3}0.181{col 68}{space 4}-.2949384{col 81}{space 3} .0557309
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .4442587{col 40}{space 2} .2052391{col 51}{space 1}    2.16{col 60}{space 3}0.030{col 68}{space 4} .0419975{col 81}{space 3} .8465199
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}-.0890108{col 40}{space 2} .1607065{col 51}{space 1}   -0.55{col 60}{space 3}0.580{col 68}{space 4}-.4039897{col 81}{space 3} .2259681
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0008137{col 40}{space 2} .0021726{col 51}{space 1}   -0.37{col 60}{space 3}0.708{col 68}{space 4}-.0050718{col 81}{space 3} .0034445
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1849888{col 40}{space 2} .0535778{col 51}{space 1}   -3.45{col 60}{space 3}0.001{col 68}{space 4}-.2899994{col 81}{space 3}-.0799782
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2}  .005759{col 40}{space 2} .0025054{col 51}{space 1}    2.30{col 60}{space 3}0.022{col 68}{space 4} .0008486{col 81}{space 3} .0106694
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000557{col 40}{space 2} .0000283{col 51}{space 1}   -1.97{col 60}{space 3}0.049{col 68}{space 4}-.0001112{col 81}{space 3}-2.36e-07
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-1.455164{col 40}{space 2} .5020748{col 51}{space 1}   -2.90{col 60}{space 3}0.004{col 68}{space 4}-2.439212{col 81}{space 3}-.4711152
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a6
{txt}
{com}. 
. * Table A1 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a1 a2 a3 a4 a5 a6 using tableA1.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A1: Alternative Post-Negotiation (De-)Escalation Measures) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. +p<0.1, *p<0.05.) 
>   nonumbers mtitles("Model 1" "Model 2" "Model 3" "Model 4" "Model 5" "Model 6")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA1.rtf not found)
(output written to {browse  `"tableA1.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}.         
. 
. 
. **********************************************************
. **********************************************************
. *** Table A2: Alternative Pre-Negotiation Time Windows ***
. **********************************************************
. **********************************************************
. 
. * Model 1 (4mo pre-negotiation window)
. logit neg_DV ib0.battles_escalation_4mo weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-453.23466}  
Iteration 1:{space 3}log pseudolikelihood = {res:-407.74374}  
Iteration 2:{space 3}log pseudolikelihood = {res:-397.94714}  
Iteration 3:{space 3}log pseudolikelihood = {res:-397.03918}  
Iteration 4:{space 3}log pseudolikelihood = {res:-396.50728}  
Iteration 5:{space 3}log pseudolikelihood = {res:-395.66699}  
Iteration 6:{space 3}log pseudolikelihood = {res:-394.69009}  
Iteration 7:{space 3}log pseudolikelihood = {res:-394.29779}  
Iteration 8:{space 3}log pseudolikelihood = {res:-394.25199}  
Iteration 9:{space 3}log pseudolikelihood = {res:-394.25163}  
Iteration 10:{space 2}log pseudolikelihood = {res:-394.25163}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,470
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    104.64
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-394.25163{txt}{col 49}Pseudo R2{col 67}= {res}    0.1301

{txt}{ralign 88:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation_4mo {c |}
{space 20}1  {c |}{col 24}{res}{space 2} -.223674{col 36}{space 2} .3113301{col 47}{space 1}   -0.72{col 56}{space 3}0.472{col 64}{space 4}-.8338698{col 77}{space 3} .3865218
{txt}{space 20}2  {c |}{col 24}{res}{space 2}-.4443767{col 36}{space 2} .1788493{col 47}{space 1}   -2.48{col 56}{space 3}0.013{col 64}{space 4} -.794915{col 77}{space 3}-.0938385
{txt}{space 22} {c |}
{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2}  .445334{col 36}{space 2} .1658453{col 47}{space 1}    2.69{col 56}{space 3}0.007{col 64}{space 4} .1202831{col 77}{space 3} .7703849
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2} .0259211{col 36}{space 2} .1235044{col 47}{space 1}    0.21{col 56}{space 3}0.834{col 64}{space 4} -.216143{col 77}{space 3} .2679852
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .4713422{col 36}{space 2} .1847304{col 47}{space 1}    2.55{col 56}{space 3}0.011{col 64}{space 4} .1092774{col 77}{space 3} .8334071
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2} .0315771{col 36}{space 2} .1613442{col 47}{space 1}    0.20{col 56}{space 3}0.845{col 64}{space 4}-.2846516{col 77}{space 3} .3478058
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0007569{col 36}{space 2} .0026532{col 47}{space 1}   -0.29{col 56}{space 3}0.775{col 64}{space 4}-.0059571{col 77}{space 3} .0044434
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.1877314{col 36}{space 2} .0475737{col 47}{space 1}   -3.95{col 56}{space 3}0.000{col 64}{space 4} -.280974{col 77}{space 3}-.0944887
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2} .0054666{col 36}{space 2} .0023392{col 47}{space 1}    2.34{col 56}{space 3}0.019{col 64}{space 4} .0008818{col 77}{space 3} .0100514
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000542{col 36}{space 2} .0000275{col 47}{space 1}   -1.97{col 56}{space 3}0.049{col 64}{space 4}-.0001081{col 77}{space 3}-2.51e-07
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}-.9915761{col 36}{space 2} .5379845{col 47}{space 1}   -1.84{col 56}{space 3}0.065{col 64}{space 4}-2.046006{col 77}{space 3} .0628541
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 109 failures and 0 successes completely determined.{p_end}

{com}. estimates store a7
{txt}
{com}. 
. * Model 2 (4mo pre-negotiation window & third-party involvement)
. logit neg_DV ib0.battles_escalation_4mo##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-360.46848}  
Iteration 1:{space 3}log pseudolikelihood = {res:-314.91526}  
Iteration 2:{space 3}log pseudolikelihood = {res:-301.37004}  
Iteration 3:{space 3}log pseudolikelihood = {res:-300.40993}  
Iteration 4:{space 3}log pseudolikelihood = {res: -299.7796}  
Iteration 5:{space 3}log pseudolikelihood = {res:-298.93565}  
Iteration 6:{space 3}log pseudolikelihood = {res:-298.21311}  
Iteration 7:{space 3}log pseudolikelihood = {res:-298.02674}  
Iteration 8:{space 3}log pseudolikelihood = {res: -298.0189}  
Iteration 9:{space 3}log pseudolikelihood = {res:-298.01889}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,173
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    155.90
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-298.01889{txt}{col 49}Pseudo R2{col 67}= {res}    0.1732

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}battles_escalation_4mo {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .5908841{col 38}{space 2} .6659073{col 49}{space 1}    0.89{col 58}{space 3}0.375{col 66}{space 4}-.7142702{col 79}{space 3} 1.896038
{txt}{space 22}2  {c |}{col 26}{res}{space 2} -.814304{col 38}{space 2} .6233644{col 49}{space 1}   -1.31{col 58}{space 3}0.191{col 66}{space 4}-2.036076{col 79}{space 3} .4074678
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .9313514{col 38}{space 2} .6090719{col 49}{space 1}    1.53{col 58}{space 3}0.126{col 66}{space 4}-.2624076{col 79}{space 3}  2.12511
{txt}{space 24} {c |}
{space 2}battles_escalation_4mo#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}-.4484969{col 38}{space 2} .8828239{col 49}{space 1}   -0.51{col 58}{space 3}0.611{col 66}{space 4}  -2.1788{col 79}{space 3} 1.281806
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} .8139104{col 38}{space 2} .6961572{col 49}{space 1}    1.17{col 58}{space 3}0.242{col 66}{space 4}-.5505327{col 79}{space 3} 2.178353
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4552618{col 38}{space 2} .2124163{col 49}{space 1}    2.14{col 58}{space 3}0.032{col 66}{space 4} .0389335{col 79}{space 3} .8715901
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2} -.063429{col 38}{space 2} .1007959{col 49}{space 1}   -0.63{col 58}{space 3}0.529{col 66}{space 4}-.2609853{col 79}{space 3} .1341272
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .4917741{col 38}{space 2} .2028919{col 49}{space 1}    2.42{col 58}{space 3}0.015{col 66}{space 4} .0941132{col 79}{space 3}  .889435
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1569263{col 38}{space 2} .1633836{col 49}{space 1}   -0.96{col 58}{space 3}0.337{col 66}{space 4}-.4771522{col 79}{space 3} .1632997
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2} .0000604{col 38}{space 2} .0023081{col 49}{space 1}    0.03{col 58}{space 3}0.979{col 66}{space 4}-.0044634{col 79}{space 3} .0045842
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1789517{col 38}{space 2}  .055081{col 49}{space 1}   -3.25{col 58}{space 3}0.001{col 66}{space 4}-.2869085{col 79}{space 3} -.070995
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0055335{col 38}{space 2} .0026039{col 49}{space 1}    2.13{col 58}{space 3}0.034{col 66}{space 4} .0004299{col 79}{space 3} .0106371
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000539{col 38}{space 2} .0000296{col 49}{space 1}   -1.82{col 58}{space 3}0.069{col 66}{space 4} -.000112{col 79}{space 3} 4.12e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.350943{col 38}{space 2} .7394972{col 49}{space 1}   -1.83{col 58}{space 3}0.068{col 66}{space 4} -2.80033{col 79}{space 3} .0984451
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a8
{txt}
{com}. 
. * Model 3 (5mo pre-negotiation window)
. logit neg_DV ib0.battles_escalation_5mo weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-450.65802}  
Iteration 1:{space 3}log pseudolikelihood = {res:-404.98599}  
Iteration 2:{space 3}log pseudolikelihood = {res:-395.03626}  
Iteration 3:{space 3}log pseudolikelihood = {res:-394.12523}  
Iteration 4:{space 3}log pseudolikelihood = {res:-393.61354}  
Iteration 5:{space 3}log pseudolikelihood = {res:-392.78703}  
Iteration 6:{space 3}log pseudolikelihood = {res:-391.82139}  
Iteration 7:{space 3}log pseudolikelihood = {res: -391.4348}  
Iteration 8:{space 3}log pseudolikelihood = {res:-391.39073}  
Iteration 9:{space 3}log pseudolikelihood = {res: -391.3904}  
Iteration 10:{space 2}log pseudolikelihood = {res: -391.3904}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,467
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    107.09
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -391.3904{txt}{col 49}Pseudo R2{col 67}= {res}    0.1315

{txt}{ralign 88:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation_5mo {c |}
{space 20}1  {c |}{col 24}{res}{space 2}-.2483965{col 36}{space 2} .3122636{col 47}{space 1}   -0.80{col 56}{space 3}0.426{col 64}{space 4}-.8604218{col 77}{space 3} .3636289
{txt}{space 20}2  {c |}{col 24}{res}{space 2}-.5032748{col 36}{space 2} .1774662{col 47}{space 1}   -2.84{col 56}{space 3}0.005{col 64}{space 4}-.8511021{col 77}{space 3}-.1554474
{txt}{space 22} {c |}
{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2}  .441152{col 36}{space 2} .1615682{col 47}{space 1}    2.73{col 56}{space 3}0.006{col 64}{space 4} .1244842{col 77}{space 3} .7578198
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2} .0182612{col 36}{space 2} .1239137{col 47}{space 1}    0.15{col 56}{space 3}0.883{col 64}{space 4}-.2246052{col 77}{space 3} .2611275
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .5104107{col 36}{space 2} .1859656{col 47}{space 1}    2.74{col 56}{space 3}0.006{col 64}{space 4} .1459249{col 77}{space 3} .8748966
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2}  .017721{col 36}{space 2} .1589394{col 47}{space 1}    0.11{col 56}{space 3}0.911{col 64}{space 4}-.2937944{col 77}{space 3} .3292364
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0009863{col 36}{space 2} .0025907{col 47}{space 1}   -0.38{col 56}{space 3}0.703{col 64}{space 4}-.0060639{col 77}{space 3} .0040913
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.1845164{col 36}{space 2} .0487925{col 47}{space 1}   -3.78{col 56}{space 3}0.000{col 64}{space 4} -.280148{col 77}{space 3}-.0888848
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2} .0053955{col 36}{space 2} .0023831{col 47}{space 1}    2.26{col 56}{space 3}0.024{col 64}{space 4} .0007248{col 77}{space 3} .0100662
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000537{col 36}{space 2} .0000278{col 47}{space 1}   -1.93{col 56}{space 3}0.054{col 64}{space 4}-.0001082{col 77}{space 3} 8.91e-07
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}-.9500204{col 36}{space 2} .5265551{col 47}{space 1}   -1.80{col 56}{space 3}0.071{col 64}{space 4}-1.982049{col 77}{space 3} .0820085
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 109 failures and 0 successes completely determined.{p_end}

{com}. estimates store a9
{txt}
{com}. 
. * Model 4 (5mo pre-negotiation window & third-party involvement)
. logit neg_DV ib0.battles_escalation_5mo##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-360.46848}  
Iteration 1:{space 3}log pseudolikelihood = {res:-314.43022}  
Iteration 2:{space 3}log pseudolikelihood = {res:-300.57726}  
Iteration 3:{space 3}log pseudolikelihood = {res:-299.60887}  
Iteration 4:{space 3}log pseudolikelihood = {res:-298.99001}  
Iteration 5:{space 3}log pseudolikelihood = {res:-298.15693}  
Iteration 6:{space 3}log pseudolikelihood = {res:-297.42733}  
Iteration 7:{space 3}log pseudolikelihood = {res:-297.23382}  
Iteration 8:{space 3}log pseudolikelihood = {res:-297.22521}  
Iteration 9:{space 3}log pseudolikelihood = {res:-297.22519}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,173
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    163.60
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-297.22519{txt}{col 49}Pseudo R2{col 67}= {res}    0.1754

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}battles_escalation_5mo {c |}
{space 22}1  {c |}{col 26}{res}{space 2}  .690089{col 38}{space 2} .7187838{col 49}{space 1}    0.96{col 58}{space 3}0.337{col 66}{space 4}-.7187015{col 79}{space 3} 2.098879
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.9212243{col 38}{space 2} .5248614{col 49}{space 1}   -1.76{col 58}{space 3}0.079{col 66}{space 4}-1.949934{col 79}{space 3} .1074852
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .9629931{col 38}{space 2}  .605422{col 49}{space 1}    1.59{col 58}{space 3}0.112{col 66}{space 4}-.2236122{col 79}{space 3} 2.149599
{txt}{space 24} {c |}
{space 2}battles_escalation_5mo#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}-.6424251{col 38}{space 2} 1.067823{col 49}{space 1}   -0.60{col 58}{space 3}0.547{col 66}{space 4}-2.735319{col 79}{space 3} 1.450469
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} .8775511{col 38}{space 2} .5758447{col 49}{space 1}    1.52{col 58}{space 3}0.128{col 66}{space 4}-.2510838{col 79}{space 3} 2.006186
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4624425{col 38}{space 2} .2236359{col 49}{space 1}    2.07{col 58}{space 3}0.039{col 66}{space 4} .0241242{col 79}{space 3} .9007609
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0710566{col 38}{space 2}  .103372{col 49}{space 1}   -0.69{col 58}{space 3}0.492{col 66}{space 4} -.273662{col 79}{space 3} .1315487
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5050347{col 38}{space 2} .1950962{col 49}{space 1}    2.59{col 58}{space 3}0.010{col 66}{space 4} .1226531{col 79}{space 3} .8874163
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1601781{col 38}{space 2} .1624445{col 49}{space 1}   -0.99{col 58}{space 3}0.324{col 66}{space 4}-.4785635{col 79}{space 3} .1582072
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0000941{col 38}{space 2} .0023576{col 49}{space 1}   -0.04{col 58}{space 3}0.968{col 66}{space 4}-.0047149{col 79}{space 3} .0045267
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1779563{col 38}{space 2} .0569408{col 49}{space 1}   -3.13{col 58}{space 3}0.002{col 66}{space 4}-.2895582{col 79}{space 3}-.0663545
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0055404{col 38}{space 2} .0026971{col 49}{space 1}    2.05{col 58}{space 3}0.040{col 66}{space 4} .0002542{col 79}{space 3} .0108266
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000544{col 38}{space 2} .0000308{col 49}{space 1}   -1.76{col 58}{space 3}0.078{col 66}{space 4}-.0001148{col 79}{space 3} 6.06e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.337245{col 38}{space 2} .7166116{col 49}{space 1}   -1.87{col 58}{space 3}0.062{col 66}{space 4}-2.741778{col 79}{space 3} .0672881
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a10
{txt}
{com}. 
. * Model 5 (6mo pre-negotiation window)
. logit neg_DV ib0.battles_escalation_6mo weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -448.0771}  
Iteration 1:{space 3}log pseudolikelihood = {res:-402.75633}  
Iteration 2:{space 3}log pseudolikelihood = {res:-392.73997}  
Iteration 3:{space 3}log pseudolikelihood = {res:-391.82792}  
Iteration 4:{space 3}log pseudolikelihood = {res:-391.32558}  
Iteration 5:{space 3}log pseudolikelihood = {res:-390.47745}  
Iteration 6:{space 3}log pseudolikelihood = {res:-389.48125}  
Iteration 7:{space 3}log pseudolikelihood = {res:-389.08055}  
Iteration 8:{space 3}log pseudolikelihood = {res:-389.03436}  
Iteration 9:{space 3}log pseudolikelihood = {res:-389.03401}  
Iteration 10:{space 2}log pseudolikelihood = {res:-389.03401}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,464
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    105.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-389.03401{txt}{col 49}Pseudo R2{col 67}= {res}    0.1318

{txt}{ralign 88:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation_6mo {c |}
{space 20}1  {c |}{col 24}{res}{space 2}-.5292952{col 36}{space 2} .3474637{col 47}{space 1}   -1.52{col 56}{space 3}0.128{col 64}{space 4}-1.210312{col 77}{space 3} .1517212
{txt}{space 20}2  {c |}{col 24}{res}{space 2}-.4770916{col 36}{space 2} .1858829{col 47}{space 1}   -2.57{col 56}{space 3}0.010{col 64}{space 4}-.8414154{col 77}{space 3}-.1127678
{txt}{space 22} {c |}
{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2} .4364994{col 36}{space 2} .1644593{col 47}{space 1}    2.65{col 56}{space 3}0.008{col 64}{space 4} .1141652{col 77}{space 3} .7588336
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2} .0025053{col 36}{space 2} .1246781{col 47}{space 1}    0.02{col 56}{space 3}0.984{col 64}{space 4}-.2418593{col 77}{space 3}   .24687
{txt}{space 16}parity {c |}{col 24}{res}{space 2}   .52237{col 36}{space 2} .1927385{col 47}{space 1}    2.71{col 56}{space 3}0.007{col 64}{space 4} .1446095{col 77}{space 3} .9001305
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2} .0350197{col 36}{space 2} .1585923{col 47}{space 1}    0.22{col 56}{space 3}0.825{col 64}{space 4}-.2758155{col 77}{space 3} .3458549
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2}-.0011381{col 36}{space 2} .0026821{col 47}{space 1}   -0.42{col 56}{space 3}0.671{col 64}{space 4} -.006395{col 77}{space 3} .0041188
{txt}{space 21}t {c |}{col 24}{res}{space 2}-.1841852{col 36}{space 2} .0482919{col 47}{space 1}   -3.81{col 56}{space 3}0.000{col 64}{space 4}-.2788356{col 77}{space 3}-.0895349
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2}  .005444{col 36}{space 2} .0023627{col 47}{space 1}    2.30{col 56}{space 3}0.021{col 64}{space 4} .0008132{col 77}{space 3} .0100748
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000545{col 36}{space 2} .0000276{col 47}{space 1}   -1.97{col 56}{space 3}0.049{col 64}{space 4}-.0001086{col 77}{space 3}-3.29e-07
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2} -.930997{col 36}{space 2} .5268618{col 47}{space 1}   -1.77{col 56}{space 3}0.077{col 64}{space 4}-1.963627{col 77}{space 3} .1016332
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 110 failures and 0 successes completely determined.{p_end}

{com}. estimates store a11
{txt}
{com}. 
. * Model 6 (6mo pre-negotiation window & third-party involvement)
. logit neg_DV ib0.battles_escalation_6mo##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-360.46848}  
Iteration 1:{space 3}log pseudolikelihood = {res:-315.52343}  
Iteration 2:{space 3}log pseudolikelihood = {res:-301.82139}  
Iteration 3:{space 3}log pseudolikelihood = {res:-300.85071}  
Iteration 4:{space 3}log pseudolikelihood = {res:-300.23098}  
Iteration 5:{space 3}log pseudolikelihood = {res:-299.38807}  
Iteration 6:{space 3}log pseudolikelihood = {res:-298.63858}  
Iteration 7:{space 3}log pseudolikelihood = {res: -298.4417}  
Iteration 8:{space 3}log pseudolikelihood = {res:-298.43289}  
Iteration 9:{space 3}log pseudolikelihood = {res:-298.43286}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,173
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    177.65
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-298.43286{txt}{col 49}Pseudo R2{col 67}= {res}    0.1721

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}battles_escalation_6mo {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .4777023{col 38}{space 2} .7237898{col 49}{space 1}    0.66{col 58}{space 3}0.509{col 66}{space 4}-.9408997{col 79}{space 3} 1.896304
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.8031021{col 38}{space 2} .5310821{col 49}{space 1}   -1.51{col 58}{space 3}0.130{col 66}{space 4}-1.844004{col 79}{space 3} .2377996
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2}  .948665{col 38}{space 2} .5697283{col 49}{space 1}    1.67{col 58}{space 3}0.096{col 66}{space 4} -.167982{col 79}{space 3} 2.065312
{txt}{space 24} {c |}
{space 2}battles_escalation_6mo#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}-.9249402{col 38}{space 2} 1.165206{col 49}{space 1}   -0.79{col 58}{space 3}0.427{col 66}{space 4}-3.208703{col 79}{space 3} 1.358822
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} .7586976{col 38}{space 2} .5677011{col 49}{space 1}    1.34{col 58}{space 3}0.181{col 66}{space 4}-.3539761{col 79}{space 3} 1.871371
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4780833{col 38}{space 2} .2217283{col 49}{space 1}    2.16{col 58}{space 3}0.031{col 66}{space 4} .0435037{col 79}{space 3} .9126628
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0925801{col 38}{space 2} .1033394{col 49}{space 1}   -0.90{col 58}{space 3}0.370{col 66}{space 4}-.2951217{col 79}{space 3} .1099615
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5154888{col 38}{space 2} .1962344{col 49}{space 1}    2.63{col 58}{space 3}0.009{col 66}{space 4} .1308764{col 79}{space 3} .9001012
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1366066{col 38}{space 2} .1583981{col 49}{space 1}   -0.86{col 58}{space 3}0.388{col 66}{space 4}-.4470612{col 79}{space 3} .1738479
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0002956{col 38}{space 2} .0023454{col 49}{space 1}   -0.13{col 58}{space 3}0.900{col 66}{space 4}-.0048925{col 79}{space 3} .0043014
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1783507{col 38}{space 2} .0574294{col 49}{space 1}   -3.11{col 58}{space 3}0.002{col 66}{space 4}-.2909102{col 79}{space 3}-.0657912
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2}  .005559{col 38}{space 2} .0027052{col 49}{space 1}    2.05{col 58}{space 3}0.040{col 66}{space 4} .0002568{col 79}{space 3} .0108612
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000546{col 38}{space 2} .0000307{col 49}{space 1}   -1.78{col 58}{space 3}0.075{col 66}{space 4}-.0001147{col 79}{space 3} 5.52e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.283956{col 38}{space 2} .6783408{col 49}{space 1}   -1.89{col 58}{space 3}0.058{col 66}{space 4} -2.61348{col 79}{space 3} .0455672
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a12
{txt}
{com}. 
. * Table A2 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a7 a8 a9 a10 a11 a12 using tableA2.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A2: Alternative Pre-Negotiation Time Windows) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. +p<0.1, *p<0.05.) 
>   nonumbers mtitles("4 Mo" "4 Mo" "5 Mo" "5 Mo" "6 Mo" "6 Mo")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA2.rtf not found)
(output written to {browse  `"tableA2.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}.         
.                 
.                 
. ***********************************************************************
. ***********************************************************************
. *** Table A3: Alternative Measure with Battle Initiator Information ***
. ***********************************************************************
. ***********************************************************************
.         
. * Model 1 
. logit neg_DV ib0.battles_escalation_i weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-405.94626}  
Iteration 1:{space 3}log pseudolikelihood = {res:-363.50028}  
Iteration 2:{space 3}log pseudolikelihood = {res:-352.64928}  
Iteration 3:{space 3}log pseudolikelihood = {res:-351.70191}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -350.993}  
Iteration 5:{space 3}log pseudolikelihood = {res:-349.70284}  
Iteration 6:{space 3}log pseudolikelihood = {res:-348.20989}  
Iteration 7:{space 3}log pseudolikelihood = {res:-347.40001}  
Iteration 8:{space 3}log pseudolikelihood = {res:-347.26206}  
Iteration 9:{space 3}log pseudolikelihood = {res:-347.26036}  
Iteration 10:{space 2}log pseudolikelihood = {res:-347.26036}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,336
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     98.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-347.26036{txt}{col 49}Pseudo R2{col 67}= {res}    0.1446

{txt}{ralign 87:(Std. Err. adjusted for {res:40} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}battles_escalation_i {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.5322016{col 35}{space 2} .2610763{col 46}{space 1}   -2.04{col 55}{space 3}0.042{col 63}{space 4}-1.043902{col 76}{space 3}-.0205015
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.5213831{col 35}{space 2} .1950339{col 46}{space 1}   -2.67{col 55}{space 3}0.008{col 63}{space 4}-.9036425{col 76}{space 3}-.1391237
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4919079{col 35}{space 2} .1674031{col 46}{space 1}    2.94{col 55}{space 3}0.003{col 63}{space 4} .1638039{col 76}{space 3} .8200119
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0159155{col 35}{space 2} .1330455{col 46}{space 1}    0.12{col 55}{space 3}0.905{col 63}{space 4}-.2448489{col 76}{space 3}   .27668
{txt}{space 15}parity {c |}{col 23}{res}{space 2}  .655968{col 35}{space 2}  .231572{col 46}{space 1}    2.83{col 55}{space 3}0.005{col 63}{space 4} .2020953{col 76}{space 3} 1.109841
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0356035{col 35}{space 2} .1577526{col 46}{space 1}    0.23{col 55}{space 3}0.821{col 63}{space 4}-.2735858{col 76}{space 3} .3447929
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0018674{col 35}{space 2} .0036037{col 46}{space 1}   -0.52{col 55}{space 3}0.604{col 63}{space 4}-.0089305{col 76}{space 3} .0051957
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2224234{col 35}{space 2} .0504027{col 46}{space 1}   -4.41{col 55}{space 3}0.000{col 63}{space 4}-.3212109{col 76}{space 3}-.1236358
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0073487{col 35}{space 2} .0025703{col 46}{space 1}    2.86{col 55}{space 3}0.004{col 63}{space 4}  .002311{col 76}{space 3} .0123864
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2} -.000075{col 35}{space 2} .0000327{col 46}{space 1}   -2.29{col 55}{space 3}0.022{col 63}{space 4}-.0001391{col 76}{space 3}-.0000108
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.7341937{col 35}{space 2} .5022493{col 46}{space 1}   -1.46{col 55}{space 3}0.144{col 63}{space 4}-1.718584{col 76}{space 3} .2501969
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 122 failures and 0 successes completely determined.{p_end}

{com}. estimates store a13
{txt}
{com}. 
. * Model 2
. logit neg_DV ib0.battles_escalation_i##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-311.19986}  
Iteration 1:{space 3}log pseudolikelihood = {res:-270.12878}  
Iteration 2:{space 3}log pseudolikelihood = {res:-256.83769}  
Iteration 3:{space 3}log pseudolikelihood = {res:-255.77917}  
Iteration 4:{space 3}log pseudolikelihood = {res:-254.81871}  
Iteration 5:{space 3}log pseudolikelihood = {res:-253.28677}  
Iteration 6:{space 3}log pseudolikelihood = {res:-252.24698}  
Iteration 7:{space 3}log pseudolikelihood = {res:-251.92954}  
Iteration 8:{space 3}log pseudolikelihood = {res:-251.91176}  
Iteration 9:{space 3}log pseudolikelihood = {res:-251.91172}  
Iteration 10:{space 2}log pseudolikelihood = {res:-251.91172}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,043
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    121.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-251.91172{txt}{col 49}Pseudo R2{col 67}= {res}    0.1905

{txt}{ralign 90:(Std. Err. adjusted for {res:34} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}battles_escalation_i {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.7036955{col 38}{space 2} .4854689{col 49}{space 1}   -1.45{col 58}{space 3}0.147{col 66}{space 4}-1.655197{col 79}{space 3} .2478061
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.693688{col 38}{space 2} .5522071{col 49}{space 1}   -3.07{col 58}{space 3}0.002{col 66}{space 4}-2.775994{col 79}{space 3}-.6113816
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .2314229{col 38}{space 2} .6885613{col 49}{space 1}    0.34{col 58}{space 3}0.737{col 66}{space 4}-1.118133{col 79}{space 3} 1.580978
{txt}{space 24} {c |}
{space 4}battles_escalation_i#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .4989851{col 38}{space 2} .6806611{col 49}{space 1}    0.73{col 58}{space 3}0.464{col 66}{space 4}-.8350863{col 79}{space 3} 1.833056
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.736447{col 38}{space 2} .7284162{col 49}{space 1}    2.38{col 58}{space 3}0.017{col 66}{space 4} .3087775{col 79}{space 3} 3.164117
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .5179805{col 38}{space 2} .2394677{col 49}{space 1}    2.16{col 58}{space 3}0.031{col 66}{space 4} .0486325{col 79}{space 3} .9873284
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.1406163{col 38}{space 2} .1286089{col 49}{space 1}   -1.09{col 58}{space 3}0.274{col 66}{space 4}-.3926851{col 79}{space 3} .1114526
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .7540189{col 38}{space 2} .3544252{col 49}{space 1}    2.13{col 58}{space 3}0.033{col 66}{space 4} .0593583{col 79}{space 3}  1.44868
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.2085169{col 38}{space 2} .1857826{col 49}{space 1}   -1.12{col 58}{space 3}0.262{col 66}{space 4} -.572644{col 79}{space 3} .1556103
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0016205{col 38}{space 2} .0037029{col 49}{space 1}   -0.44{col 58}{space 3}0.662{col 66}{space 4} -.008878{col 79}{space 3} .0056371
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.2146243{col 38}{space 2} .0610133{col 49}{space 1}   -3.52{col 58}{space 3}0.000{col 66}{space 4}-.3342081{col 79}{space 3}-.0950404
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0073038{col 38}{space 2} .0028015{col 49}{space 1}    2.61{col 58}{space 3}0.009{col 66}{space 4} .0018131{col 79}{space 3} .0127946
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000713{col 38}{space 2}  .000032{col 49}{space 1}   -2.23{col 58}{space 3}0.026{col 66}{space 4} -.000134{col 79}{space 3}-8.49e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.2431754{col 38}{space 2} .8272796{col 49}{space 1}   -0.29{col 58}{space 3}0.769{col 66}{space 4}-1.864614{col 79}{space 3} 1.378263
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 86 failures and 0 successes completely determined.{p_end}

{com}. estimates store a14
{txt}
{com}.                 
. * Table A3 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a13 a14 using tableA3.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A3: (De-)Escalation: Battle Initiator) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. +p<0.1, *p<0.05.) 
>   nonumbers mtitles("Model 1" "Model 2")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA3.rtf not found)
(output written to {browse  `"tableA3.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
.         
. ***************************************************************
. ***************************************************************
. *** Table A4: Alternative Measures of 3rd Party Involvement ***
. ***************************************************************
. ***************************************************************
.                 
. * Model 1 (strong involvement) 
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement_strong weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-328.28846}  
Iteration 2:{space 3}log pseudolikelihood = {res:-300.64628}  
Iteration 3:{space 3}log pseudolikelihood = {res:-299.67774}  
Iteration 4:{space 3}log pseudolikelihood = {res:-299.08934}  
Iteration 5:{space 3}log pseudolikelihood = {res:-298.24958}  
Iteration 6:{space 3}log pseudolikelihood = {res:-297.54934}  
Iteration 7:{space 3}log pseudolikelihood = {res:-297.30834}  
Iteration 8:{space 3}log pseudolikelihood = {res:-297.29768}  
Iteration 9:{space 3}log pseudolikelihood = {res:-297.29768}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    195.19
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-297.29768{txt}{col 49}Pseudo R2{col 67}= {res}    0.1919

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2}-.2636868{col 40}{space 2} .3552627{col 51}{space 1}   -0.74{col 60}{space 3}0.458{col 68}{space 4}-.9599889{col 81}{space 3} .4326154
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.6963196{col 40}{space 2} .3230011{col 51}{space 1}   -2.16{col 60}{space 3}0.031{col 68}{space 4} -1.32939{col 81}{space 3} -.063249
{txt}{space 26} {c |}
1.thirdparty_involvement~g {c |}{col 28}{res}{space 2} 1.027715{col 40}{space 2} .4215607{col 51}{space 1}    2.44{col 60}{space 3}0.015{col 68}{space 4} .2014717{col 81}{space 3} 1.853959
{txt}{space 26} {c |}
{space 8}battles_escalation#{c |}
thirdparty_involvement_s~g {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .0515199{col 40}{space 2} .6115233{col 51}{space 1}    0.08{col 60}{space 3}0.933{col 68}{space 4}-1.147044{col 81}{space 3} 1.250084
{txt}{space 22}2 1  {c |}{col 28}{res}{space 2} .9507366{col 40}{space 2} .5179453{col 51}{space 1}    1.84{col 60}{space 3}0.066{col 68}{space 4}-.0644175{col 81}{space 3} 1.965891
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .3070517{col 40}{space 2} .2767853{col 51}{space 1}    1.11{col 60}{space 3}0.267{col 68}{space 4}-.2354376{col 81}{space 3} .8495409
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.1724371{col 40}{space 2} .1093174{col 51}{space 1}   -1.58{col 60}{space 3}0.115{col 68}{space 4}-.3866953{col 81}{space 3}  .041821
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .2555419{col 40}{space 2}  .195399{col 51}{space 1}    1.31{col 60}{space 3}0.191{col 68}{space 4}-.1274332{col 81}{space 3}  .638517
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}-.0632708{col 40}{space 2} .1650604{col 51}{space 1}   -0.38{col 60}{space 3}0.701{col 68}{space 4}-.3867833{col 81}{space 3} .2602416
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2} .0003547{col 40}{space 2} .0024904{col 51}{space 1}    0.14{col 60}{space 3}0.887{col 68}{space 4}-.0045264{col 81}{space 3} .0052359
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1740358{col 40}{space 2} .0648999{col 51}{space 1}   -2.68{col 60}{space 3}0.007{col 68}{space 4}-.3012373{col 81}{space 3}-.0468343
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0054585{col 40}{space 2} .0028746{col 51}{space 1}    1.90{col 60}{space 3}0.058{col 68}{space 4}-.0001756{col 81}{space 3} .0110926
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000544{col 40}{space 2} .0000318{col 51}{space 1}   -1.71{col 60}{space 3}0.087{col 68}{space 4}-.0001167{col 81}{space 3} 7.90e-06
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-.9709722{col 40}{space 2} .6217399{col 51}{space 1}   -1.56{col 60}{space 3}0.118{col 68}{space 4} -2.18956{col 81}{space 3} .2476157
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 83 failures and 0 successes completely determined.{p_end}

{com}. estimates store a15
{txt}
{com}. 
. * Model 2 (Mediation)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement_mediate weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-319.85182}  
Iteration 2:{space 3}log pseudolikelihood = {res:-303.83561}  
Iteration 3:{space 3}log pseudolikelihood = {res:-302.80779}  
Iteration 4:{space 3}log pseudolikelihood = {res:-302.18793}  
Iteration 5:{space 3}log pseudolikelihood = {res:-301.27717}  
Iteration 6:{space 3}log pseudolikelihood = {res:-300.63302}  
Iteration 7:{space 3}log pseudolikelihood = {res:-300.43843}  
Iteration 8:{space 3}log pseudolikelihood = {res:-300.43073}  
Iteration 9:{space 3}log pseudolikelihood = {res:-300.43071}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    165.38
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-300.43071{txt}{col 49}Pseudo R2{col 67}= {res}    0.1834

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2}-.0558875{col 40}{space 2} .4270407{col 51}{space 1}   -0.13{col 60}{space 3}0.896{col 68}{space 4}-.8928718{col 81}{space 3} .7810969
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-1.058976{col 40}{space 2} .3661469{col 51}{space 1}   -2.89{col 60}{space 3}0.004{col 68}{space 4}-1.776611{col 81}{space 3} -.341341
{txt}{space 26} {c |}
1.thirdparty_involvemen~te {c |}{col 28}{res}{space 2} .8453406{col 40}{space 2} .4837036{col 51}{space 1}    1.75{col 60}{space 3}0.081{col 68}{space 4} -.102701{col 81}{space 3} 1.793382
{txt}{space 26} {c |}
{space 8}battles_escalation#{c |}
thirdparty_involvement_m~e {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2}-.0493454{col 40}{space 2} .7210775{col 51}{space 1}   -0.07{col 60}{space 3}0.945{col 68}{space 4}-1.462631{col 81}{space 3} 1.363941
{txt}{space 22}2 1  {c |}{col 28}{res}{space 2} 1.039039{col 40}{space 2} .4097735{col 51}{space 1}    2.54{col 60}{space 3}0.011{col 68}{space 4}  .235898{col 81}{space 3} 1.842181
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2}  .525867{col 40}{space 2} .1842821{col 51}{space 1}    2.85{col 60}{space 3}0.004{col 68}{space 4} .1646807{col 81}{space 3} .8870533
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.0022072{col 40}{space 2} .0869448{col 51}{space 1}   -0.03{col 60}{space 3}0.980{col 68}{space 4}-.1726158{col 81}{space 3} .1682013
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .4893229{col 40}{space 2} .1844493{col 51}{space 1}    2.65{col 60}{space 3}0.008{col 68}{space 4} .1278089{col 81}{space 3} .8508369
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}     -.15{col 40}{space 2} .1698562{col 51}{space 1}   -0.88{col 60}{space 3}0.377{col 68}{space 4}-.4829119{col 81}{space 3}  .182912
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0016922{col 40}{space 2} .0024567{col 51}{space 1}   -0.69{col 60}{space 3}0.491{col 68}{space 4}-.0065072{col 81}{space 3} .0031229
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1774392{col 40}{space 2} .0513296{col 51}{space 1}   -3.46{col 60}{space 3}0.001{col 68}{space 4}-.2780433{col 81}{space 3}-.0768351
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0054164{col 40}{space 2} .0024176{col 51}{space 1}    2.24{col 60}{space 3}0.025{col 68}{space 4}  .000678{col 81}{space 3} .0101548
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000521{col 40}{space 2} .0000269{col 51}{space 1}   -1.94{col 60}{space 3}0.052{col 68}{space 4}-.0001048{col 81}{space 3} 5.43e-07
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-1.108628{col 40}{space 2}  .548174{col 51}{space 1}   -2.02{col 60}{space 3}0.043{col 68}{space 4}-2.183029{col 81}{space 3}-.0342269
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 81 failures and 0 successes completely determined.{p_end}

{com}. estimates store a16
{txt}
{com}. 
. * Model 3 (Security Guarantees) 
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement_enforce weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-367.88362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-329.03567}  
Iteration 2:{space 3}log pseudolikelihood = {res: -319.0099}  
Iteration 3:{space 3}log pseudolikelihood = {res:-318.12858}  
Iteration 4:{space 3}log pseudolikelihood = {res:-317.48968}  
Iteration 5:{space 3}log pseudolikelihood = {res:-316.56863}  
Iteration 6:{space 3}log pseudolikelihood = {res:-315.78449}  
Iteration 7:{space 3}log pseudolikelihood = {res:-315.57036}  
Iteration 8:{space 3}log pseudolikelihood = {res:-315.56258}  
Iteration 9:{space 3}log pseudolikelihood = {res:-315.56255}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    135.57
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-315.56255{txt}{col 49}Pseudo R2{col 67}= {res}    0.1422

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2}-.4277224{col 40}{space 2} .2972665{col 51}{space 1}   -1.44{col 60}{space 3}0.150{col 68}{space 4}-1.010354{col 81}{space 3} .1549092
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.4949394{col 40}{space 2} .2314697{col 51}{space 1}   -2.14{col 60}{space 3}0.032{col 68}{space 4}-.9486116{col 81}{space 3}-.0412672
{txt}{space 26} {c |}
1.thirdparty_involvemen~ce {c |}{col 28}{res}{space 2} .0678336{col 40}{space 2} .3404197{col 51}{space 1}    0.20{col 60}{space 3}0.842{col 68}{space 4}-.5993768{col 81}{space 3} .7350439
{txt}{space 26} {c |}
{space 8}battles_escalation#{c |}
thirdparty_involvement_e~e {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .3474274{col 40}{space 2} .6839972{col 51}{space 1}    0.51{col 60}{space 3}0.611{col 68}{space 4}-.9931824{col 81}{space 3} 1.688037
{txt}{space 22}2 1  {c |}{col 28}{res}{space 2} .4349441{col 40}{space 2} .3939699{col 51}{space 1}    1.10{col 60}{space 3}0.270{col 68}{space 4}-.3372227{col 81}{space 3} 1.207111
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .5449796{col 40}{space 2} .1901134{col 51}{space 1}    2.87{col 60}{space 3}0.004{col 68}{space 4} .1723641{col 81}{space 3} .9175951
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.1090188{col 40}{space 2} .1156535{col 51}{space 1}   -0.94{col 60}{space 3}0.346{col 68}{space 4}-.3356955{col 81}{space 3} .1176579
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .5335631{col 40}{space 2} .2190949{col 51}{space 1}    2.44{col 60}{space 3}0.015{col 68}{space 4}  .104145{col 81}{space 3} .9629813
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}-.0507541{col 40}{space 2} .1584665{col 51}{space 1}   -0.32{col 60}{space 3}0.749{col 68}{space 4}-.3613427{col 81}{space 3} .2598346
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0000833{col 40}{space 2} .0022934{col 51}{space 1}   -0.04{col 60}{space 3}0.971{col 68}{space 4}-.0045783{col 81}{space 3} .0044117
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.2037543{col 40}{space 2} .0605512{col 51}{space 1}   -3.36{col 60}{space 3}0.001{col 68}{space 4}-.3224325{col 81}{space 3}-.0850761
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0060966{col 40}{space 2} .0027809{col 51}{space 1}    2.19{col 60}{space 3}0.028{col 68}{space 4}  .000646{col 81}{space 3} .0115471
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000591{col 40}{space 2} .0000316{col 51}{space 1}   -1.87{col 60}{space 3}0.061{col 68}{space 4} -.000121{col 81}{space 3} 2.76e-06
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-.5621001{col 40}{space 2} .5271641{col 51}{space 1}   -1.07{col 60}{space 3}0.286{col 68}{space 4}-1.595323{col 81}{space 3} .4711225
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 84 failures and 0 successes completely determined.{p_end}

{com}. estimates store a17
{txt}
{com}. 
. * Table A4 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a15 a16 a17 using tableA4.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A4: Alternative Third-Party Measures) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. +p<0.1, *p<0.05.) 
>   nonumbers mtitles("Strong Involvement" "Mediation" "Security Guarantees")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA4.rtf not found)
(output written to {browse  `"tableA4.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}.         
. 
. 
. ****************************************************************************
. ****************************************************************************
. *** Table A5: Alternative Measures of Political Outcomes of Negotiations ***    
. ****************************************************************************
. ****************************************************************************
.         
. * Model 1 (strong concessions)
. logit neg_DV ib0.battles_escalation strong_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.44135}  
Iteration 1:{space 3}log pseudolikelihood = {res:-409.60624}  
Iteration 2:{space 3}log pseudolikelihood = {res:-398.23282}  
Iteration 3:{space 3}log pseudolikelihood = {res:-397.17576}  
Iteration 4:{space 3}log pseudolikelihood = {res:-396.84166}  
Iteration 5:{space 3}log pseudolikelihood = {res:-396.14101}  
Iteration 6:{space 3}log pseudolikelihood = {res:-394.98813}  
Iteration 7:{space 3}log pseudolikelihood = {res:-394.12594}  
Iteration 8:{space 3}log pseudolikelihood = {res:-393.90257}  
Iteration 9:{space 3}log pseudolikelihood = {res:-393.89382}  
Iteration 10:{space 2}log pseudolikelihood = {res:-393.89382}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,444
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    143.10
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-393.89382{txt}{col 49}Pseudo R2{col 67}= {res}    0.1389

{txt}{ralign 89:(Std. Err. adjusted for {res:41} clusters in dyadid)}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                 neg_DV{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}battles_escalation {c |}
{space 21}1  {c |}{col 25}{res}{space 2} -.455687{col 37}{space 2} .2355421{col 48}{space 1}   -1.93{col 57}{space 3}0.053{col 65}{space 4}-.9173409{col 78}{space 3}  .005967
{txt}{space 21}2  {c |}{col 25}{res}{space 2}-.4000213{col 37}{space 2} .1605336{col 48}{space 1}   -2.49{col 57}{space 3}0.013{col 65}{space 4}-.7146614{col 78}{space 3}-.0853812
{txt}{space 23} {c |}
strong_con_last_neg_dum {c |}{col 25}{res}{space 2} .4415407{col 37}{space 2} .1980308{col 48}{space 1}    2.23{col 57}{space 3}0.026{col 65}{space 4} .0534074{col 78}{space 3} .8296739
{txt}{space 11}str_centcomm {c |}{col 25}{res}{space 2} .0148586{col 37}{space 2} .1134614{col 48}{space 1}    0.13{col 57}{space 3}0.896{col 65}{space 4}-.2075216{col 78}{space 3} .2372387
{txt}{space 17}parity {c |}{col 25}{res}{space 2} .4938302{col 37}{space 2} .1723987{col 48}{space 1}    2.86{col 57}{space 3}0.004{col 65}{space 4}  .155935{col 78}{space 3} .8317254
{txt}{space 15}groupnum {c |}{col 25}{res}{space 2}-.0012887{col 37}{space 2} .1378031{col 48}{space 1}   -0.01{col 57}{space 3}0.993{col 65}{space 4}-.2713778{col 78}{space 3} .2688004
{txt}{space 12}dyad_months {c |}{col 25}{res}{space 2}-.0008962{col 37}{space 2} .0025205{col 48}{space 1}   -0.36{col 57}{space 3}0.722{col 65}{space 4}-.0058362{col 78}{space 3} .0040439
{txt}{space 22}t {c |}{col 25}{res}{space 2}-.2216187{col 37}{space 2}  .046919{col 48}{space 1}   -4.72{col 57}{space 3}0.000{col 65}{space 4}-.3135783{col 78}{space 3}-.1296591
{txt}{space 23} {c |}
{space 16}c.t#c.t {c |}{col 25}{res}{space 2} .0077113{col 37}{space 2} .0023157{col 48}{space 1}    3.33{col 57}{space 3}0.001{col 65}{space 4} .0031725{col 78}{space 3}   .01225
{txt}{space 23} {c |}
{space 12}c.t#c.t#c.t {c |}{col 25}{res}{space 2}-.0000898{col 37}{space 2} .0000317{col 48}{space 1}   -2.84{col 57}{space 3}0.005{col 65}{space 4}-.0001518{col 78}{space 3}-.0000277
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-.7162002{col 37}{space 2} .4607721{col 48}{space 1}   -1.55{col 57}{space 3}0.120{col 65}{space 4}-1.619297{col 78}{space 3} .1868964
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 142 failures and 0 successes completely determined.{p_end}

{com}. estimates store p1
{txt}
{com}. 
. * Model 2 (strong concessions & third party involvement)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement strong_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-358.61749}  
Iteration 1:{space 3}log pseudolikelihood = {res:-311.66901}  
Iteration 2:{space 3}log pseudolikelihood = {res: -298.0631}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.12685}  
Iteration 4:{space 3}log pseudolikelihood = {res: -296.6714}  
Iteration 5:{space 3}log pseudolikelihood = {res:-295.93557}  
Iteration 6:{space 3}log pseudolikelihood = {res: -294.9226}  
Iteration 7:{space 3}log pseudolikelihood = {res:-294.40671}  
Iteration 8:{space 3}log pseudolikelihood = {res:-294.33659}  
Iteration 9:{space 3}log pseudolikelihood = {res:-294.33582}  
Iteration 10:{space 2}log pseudolikelihood = {res:-294.33582}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,154
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    199.84
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-294.33582{txt}{col 49}Pseudo R2{col 67}= {res}    0.1792

{txt}{ralign 90:(Std. Err. adjusted for {res:34} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.3759126{col 38}{space 2} .5029636{col 49}{space 1}   -0.75{col 58}{space 3}0.455{col 66}{space 4}-1.361703{col 79}{space 3} .6098779
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.323663{col 38}{space 2}  .393808{col 49}{space 1}   -3.36{col 58}{space 3}0.001{col 66}{space 4}-2.095512{col 79}{space 3}-.5518135
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .5723765{col 38}{space 2} .5378496{col 49}{space 1}    1.06{col 58}{space 3}0.287{col 66}{space 4}-.4817893{col 79}{space 3} 1.626542
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .3016955{col 38}{space 2}  .694404{col 49}{space 1}    0.43{col 58}{space 3}0.664{col 66}{space 4}-1.059311{col 79}{space 3} 1.662702
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.332751{col 38}{space 2} .4925908{col 49}{space 1}    2.71{col 58}{space 3}0.007{col 66}{space 4} .3672911{col 79}{space 3} 2.298211
{txt}{space 24} {c |}
{space 1}strong_con_last_neg_dum {c |}{col 26}{res}{space 2} .3077161{col 38}{space 2} .2253733{col 49}{space 1}    1.37{col 58}{space 3}0.172{col 66}{space 4}-.1340074{col 79}{space 3} .7494396
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0936768{col 38}{space 2} .0944631{col 49}{space 1}   -0.99{col 58}{space 3}0.321{col 66}{space 4}-.2788211{col 79}{space 3} .0914675
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .4628196{col 38}{space 2} .2007512{col 49}{space 1}    2.31{col 58}{space 3}0.021{col 66}{space 4} .0693545{col 79}{space 3} .8562847
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2} -.131102{col 38}{space 2} .1683779{col 49}{space 1}   -0.78{col 58}{space 3}0.436{col 66}{space 4}-.4611166{col 79}{space 3} .1989125
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0010328{col 38}{space 2} .0023067{col 49}{space 1}   -0.45{col 58}{space 3}0.654{col 66}{space 4}-.0055538{col 79}{space 3} .0034883
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.2109384{col 38}{space 2} .0530775{col 49}{space 1}   -3.97{col 58}{space 3}0.000{col 66}{space 4}-.3149684{col 79}{space 3}-.1069084
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0074781{col 38}{space 2} .0024782{col 49}{space 1}    3.02{col 58}{space 3}0.003{col 66}{space 4} .0026209{col 79}{space 3} .0123353
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000831{col 38}{space 2} .0000279{col 49}{space 1}   -2.98{col 58}{space 3}0.003{col 66}{space 4}-.0001378{col 79}{space 3}-.0000283
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.7186159{col 38}{space 2} .6039437{col 49}{space 1}   -1.19{col 58}{space 3}0.234{col 66}{space 4}-1.902324{col 79}{space 3} .4650919
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 95 failures and 0 successes completely determined.{p_end}

{com}. estimates stor p2
{txt}
{com}. 
. * Model 3 (political concessions)
. logit neg_DV ib0.battles_escalation pol_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.44135}  
Iteration 1:{space 3}log pseudolikelihood = {res:-408.70335}  
Iteration 2:{space 3}log pseudolikelihood = {res:-396.37474}  
Iteration 3:{space 3}log pseudolikelihood = {res:-395.27746}  
Iteration 4:{space 3}log pseudolikelihood = {res:-394.95803}  
Iteration 5:{space 3}log pseudolikelihood = {res:-394.46612}  
Iteration 6:{space 3}log pseudolikelihood = {res:-393.39472}  
Iteration 7:{space 3}log pseudolikelihood = {res:-392.49649}  
Iteration 8:{space 3}log pseudolikelihood = {res: -392.2351}  
Iteration 9:{space 3}log pseudolikelihood = {res:-392.22207}  
Iteration 10:{space 2}log pseudolikelihood = {res:-392.22206}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,444
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    135.92
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-392.22206{txt}{col 49}Pseudo R2{col 67}= {res}    0.1426

{txt}{ralign 86:(Std. Err. adjusted for {res:41} clusters in dyadid)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}              neg_DV{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}battles_escalation {c |}
{space 18}1  {c |}{col 22}{res}{space 2}-.4611931{col 34}{space 2} .2427372{col 45}{space 1}   -1.90{col 54}{space 3}0.057{col 62}{space 4}-.9369493{col 75}{space 3}  .014563
{txt}{space 18}2  {c |}{col 22}{res}{space 2}-.4753372{col 34}{space 2}  .159904{col 45}{space 1}   -2.97{col 54}{space 3}0.003{col 62}{space 4}-.7887432{col 75}{space 3}-.1619311
{txt}{space 20} {c |}
pol_con_last_neg_dum {c |}{col 22}{res}{space 2} .6365811{col 34}{space 2} .2031346{col 45}{space 1}    3.13{col 54}{space 3}0.002{col 62}{space 4} .2384446{col 75}{space 3} 1.034718
{txt}{space 8}str_centcomm {c |}{col 22}{res}{space 2} .0271255{col 34}{space 2} .1126927{col 45}{space 1}    0.24{col 54}{space 3}0.810{col 62}{space 4}-.1937481{col 75}{space 3} .2479992
{txt}{space 14}parity {c |}{col 22}{res}{space 2} .4516018{col 34}{space 2} .1651282{col 45}{space 1}    2.73{col 54}{space 3}0.006{col 62}{space 4} .1279564{col 75}{space 3} .7752471
{txt}{space 12}groupnum {c |}{col 22}{res}{space 2} .0220169{col 34}{space 2} .1392674{col 45}{space 1}    0.16{col 54}{space 3}0.874{col 62}{space 4}-.2509421{col 75}{space 3} .2949759
{txt}{space 9}dyad_months {c |}{col 22}{res}{space 2}-.0010281{col 34}{space 2} .0024386{col 45}{space 1}   -0.42{col 54}{space 3}0.673{col 62}{space 4}-.0058077{col 75}{space 3} .0037515
{txt}{space 19}t {c |}{col 22}{res}{space 2} -.213592{col 34}{space 2} .0493028{col 45}{space 1}   -4.33{col 54}{space 3}0.000{col 62}{space 4}-.3102237{col 75}{space 3}-.1169603
{txt}{space 20} {c |}
{space 13}c.t#c.t {c |}{col 22}{res}{space 2} .0073453{col 34}{space 2} .0024366{col 45}{space 1}    3.01{col 54}{space 3}0.003{col 62}{space 4} .0025696{col 75}{space 3}  .012121
{txt}{space 20} {c |}
{space 9}c.t#c.t#c.t {c |}{col 22}{res}{space 2}-.0000852{col 34}{space 2} .0000328{col 45}{space 1}   -2.60{col 54}{space 3}0.009{col 62}{space 4}-.0001494{col 75}{space 3}-.0000209
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-.7922288{col 34}{space 2} .4714426{col 45}{space 1}   -1.68{col 54}{space 3}0.093{col 62}{space 4}-1.716239{col 75}{space 3} .1317816
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 139 failures and 0 successes completely determined.{p_end}

{com}. estimates store p3
{txt}
{com}. 
. * Model 4 (political concessions & third party involvement)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement pol_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-358.61749}  
Iteration 1:{space 3}log pseudolikelihood = {res:-311.56364}  
Iteration 2:{space 3}log pseudolikelihood = {res:-296.75572}  
Iteration 3:{space 3}log pseudolikelihood = {res:-295.78327}  
Iteration 4:{space 3}log pseudolikelihood = {res:-295.31836}  
Iteration 5:{space 3}log pseudolikelihood = {res:-294.57592}  
Iteration 6:{space 3}log pseudolikelihood = {res:-293.54134}  
Iteration 7:{space 3}log pseudolikelihood = {res:-293.00537}  
Iteration 8:{space 3}log pseudolikelihood = {res:-292.93102}  
Iteration 9:{space 3}log pseudolikelihood = {res:-292.93017}  
Iteration 10:{space 2}log pseudolikelihood = {res:-292.93017}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,154
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    174.50
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-292.93017{txt}{col 49}Pseudo R2{col 67}= {res}    0.1832

{txt}{ralign 90:(Std. Err. adjusted for {res:34} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.2728409{col 38}{space 2} .4811809{col 49}{space 1}   -0.57{col 58}{space 3}0.571{col 66}{space 4}-1.215938{col 79}{space 3} .6702564
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.330733{col 38}{space 2} .3824848{col 49}{space 1}   -3.48{col 58}{space 3}0.001{col 66}{space 4}-2.080389{col 79}{space 3}-.5810761
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .6088733{col 38}{space 2} .4929855{col 49}{space 1}    1.24{col 58}{space 3}0.217{col 66}{space 4}-.3573606{col 79}{space 3} 1.575107
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}  .098183{col 38}{space 2} .6934563{col 49}{space 1}    0.14{col 58}{space 3}0.887{col 66}{space 4}-1.260966{col 79}{space 3} 1.457332
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.281595{col 38}{space 2} .4819579{col 49}{space 1}    2.66{col 58}{space 3}0.008{col 66}{space 4} .3369752{col 79}{space 3} 2.226215
{txt}{space 24} {c |}
{space 4}pol_con_last_neg_dum {c |}{col 26}{res}{space 2} .5741666{col 38}{space 2} .2610817{col 49}{space 1}    2.20{col 58}{space 3}0.028{col 66}{space 4} .0624558{col 79}{space 3} 1.085877
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0771696{col 38}{space 2} .0948535{col 49}{space 1}   -0.81{col 58}{space 3}0.416{col 66}{space 4} -.263079{col 79}{space 3} .1087399
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5021816{col 38}{space 2} .2020053{col 49}{space 1}    2.49{col 58}{space 3}0.013{col 66}{space 4} .1062584{col 79}{space 3} .8981048
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1352981{col 38}{space 2} .1685761{col 49}{space 1}   -0.80{col 58}{space 3}0.422{col 66}{space 4}-.4657013{col 79}{space 3}  .195105
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0013307{col 38}{space 2} .0022273{col 49}{space 1}   -0.60{col 58}{space 3}0.550{col 66}{space 4}-.0056962{col 79}{space 3} .0030348
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.2098203{col 38}{space 2} .0557301{col 49}{space 1}   -3.76{col 58}{space 3}0.000{col 66}{space 4}-.3190494{col 79}{space 3}-.1005912
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0075091{col 38}{space 2} .0025933{col 49}{space 1}    2.90{col 58}{space 3}0.004{col 66}{space 4} .0024263{col 79}{space 3} .0125919
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000836{col 38}{space 2}  .000029{col 49}{space 1}   -2.88{col 58}{space 3}0.004{col 66}{space 4}-.0001405{col 79}{space 3}-.0000267
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.7899907{col 38}{space 2} .5873847{col 49}{space 1}   -1.34{col 58}{space 3}0.179{col 66}{space 4}-1.941244{col 79}{space 3} .3612622
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 95 failures and 0 successes completely determined.{p_end}

{com}. estimates store p4
{txt}
{com}. 
. * Model 5 (number of concessions)
. logit neg_DV ib0.battles_escalation weak_con_last_neg str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-457.44135}  
Iteration 1:{space 3}log pseudolikelihood = {res:-410.37697}  
Iteration 2:{space 3}log pseudolikelihood = {res:-398.42457}  
Iteration 3:{space 3}log pseudolikelihood = {res:-397.29186}  
Iteration 4:{space 3}log pseudolikelihood = {res:-396.95204}  
Iteration 5:{space 3}log pseudolikelihood = {res:-396.42029}  
Iteration 6:{space 3}log pseudolikelihood = {res:-395.29703}  
Iteration 7:{space 3}log pseudolikelihood = {res: -394.3096}  
Iteration 8:{space 3}log pseudolikelihood = {res:-393.99371}  
Iteration 9:{space 3}log pseudolikelihood = {res:-393.97643}  
Iteration 10:{space 2}log pseudolikelihood = {res:-393.97641}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,444
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    118.70
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-393.97641{txt}{col 49}Pseudo R2{col 67}= {res}    0.1387

{txt}{ralign 84:(Std. Err. adjusted for {res:41} clusters in dyadid)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            neg_DV{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation {c |}
{space 16}1  {c |}{col 20}{res}{space 2}-.4411427{col 32}{space 2} .2361678{col 43}{space 1}   -1.87{col 52}{space 3}0.062{col 60}{space 4}-.9040232{col 73}{space 3} .0217377
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.3701002{col 32}{space 2} .1660366{col 43}{space 1}   -2.23{col 52}{space 3}0.026{col 60}{space 4}-.6955259{col 73}{space 3}-.0446746
{txt}{space 18} {c |}
{space 1}weak_con_last_neg {c |}{col 20}{res}{space 2} .0564882{col 32}{space 2} .0354042{col 43}{space 1}    1.60{col 52}{space 3}0.111{col 60}{space 4}-.0129028{col 73}{space 3} .1258792
{txt}{space 6}str_centcomm {c |}{col 20}{res}{space 2} .0132263{col 32}{space 2} .1115696{col 43}{space 1}    0.12{col 52}{space 3}0.906{col 60}{space 4}-.2054462{col 73}{space 3} .2318987
{txt}{space 12}parity {c |}{col 20}{res}{space 2} .4644902{col 32}{space 2} .1658846{col 43}{space 1}    2.80{col 52}{space 3}0.005{col 60}{space 4} .1393624{col 73}{space 3}  .789618
{txt}{space 10}groupnum {c |}{col 20}{res}{space 2} .0220734{col 32}{space 2} .1355075{col 43}{space 1}    0.16{col 52}{space 3}0.871{col 60}{space 4}-.2435164{col 73}{space 3} .2876632
{txt}{space 7}dyad_months {c |}{col 20}{res}{space 2}-.0012126{col 32}{space 2} .0023276{col 43}{space 1}   -0.52{col 52}{space 3}0.602{col 60}{space 4}-.0057747{col 73}{space 3} .0033495
{txt}{space 17}t {c |}{col 20}{res}{space 2}-.2236629{col 32}{space 2} .0481481{col 43}{space 1}   -4.65{col 52}{space 3}0.000{col 60}{space 4}-.3180314{col 73}{space 3}-.1292943
{txt}{space 18} {c |}
{space 11}c.t#c.t {c |}{col 20}{res}{space 2} .0077452{col 32}{space 2} .0024116{col 43}{space 1}    3.21{col 52}{space 3}0.001{col 60}{space 4} .0030186{col 73}{space 3} .0124719
{txt}{space 18} {c |}
{space 7}c.t#c.t#c.t {c |}{col 20}{res}{space 2}  -.00009{col 32}{space 2} .0000332{col 43}{space 1}   -2.71{col 52}{space 3}0.007{col 60}{space 4}-.0001549{col 73}{space 3} -.000025
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-.6951599{col 32}{space 2}  .453636{col 43}{space 1}   -1.53{col 52}{space 3}0.125{col 60}{space 4} -1.58427{col 73}{space 3} .1939504
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 142 failures and 0 successes completely determined.{p_end}

{com}. estimates store p5
{txt}
{com}. 
. * Model 6 (number of concessions & third party involvement)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-358.61749}  
Iteration 1:{space 3}log pseudolikelihood = {res:-312.00272}  
Iteration 2:{space 3}log pseudolikelihood = {res:-298.27662}  
Iteration 3:{space 3}log pseudolikelihood = {res: -297.3313}  
Iteration 4:{space 3}log pseudolikelihood = {res:-296.86623}  
Iteration 5:{space 3}log pseudolikelihood = {res:-296.12368}  
Iteration 6:{space 3}log pseudolikelihood = {res:-295.10696}  
Iteration 7:{space 3}log pseudolikelihood = {res:-294.58956}  
Iteration 8:{space 3}log pseudolikelihood = {res:-294.51837}  
Iteration 9:{space 3}log pseudolikelihood = {res:-294.51757}  
Iteration 10:{space 2}log pseudolikelihood = {res:-294.51757}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,154
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    175.08
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-294.51757{txt}{col 49}Pseudo R2{col 67}= {res}    0.1787

{txt}{ralign 90:(Std. Err. adjusted for {res:34} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.3565051{col 38}{space 2} .5099366{col 49}{space 1}   -0.70{col 58}{space 3}0.484{col 66}{space 4}-1.355962{col 79}{space 3} .6429523
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.332394{col 38}{space 2} .4077014{col 49}{space 1}   -3.27{col 58}{space 3}0.001{col 66}{space 4}-2.131474{col 79}{space 3}-.5333135
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .5498254{col 38}{space 2} .5603296{col 49}{space 1}    0.98{col 58}{space 3}0.326{col 66}{space 4}-.5484004{col 79}{space 3} 1.648051
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}  .302891{col 38}{space 2} .6772417{col 49}{space 1}    0.45{col 58}{space 3}0.655{col 66}{space 4}-1.024478{col 79}{space 3}  1.63026
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.383786{col 38}{space 2} .4900326{col 49}{space 1}    2.82{col 58}{space 3}0.005{col 66}{space 4} .4233395{col 79}{space 3} 2.344232
{txt}{space 24} {c |}
{space 7}weak_con_last_neg {c |}{col 26}{res}{space 2} .0345583{col 38}{space 2} .0413786{col 49}{space 1}    0.84{col 58}{space 3}0.404{col 66}{space 4}-.0465424{col 79}{space 3} .1156589
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0753643{col 38}{space 2} .0936911{col 49}{space 1}   -0.80{col 58}{space 3}0.421{col 66}{space 4}-.2589955{col 79}{space 3} .1082669
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .4297658{col 38}{space 2} .2057477{col 49}{space 1}    2.09{col 58}{space 3}0.037{col 66}{space 4} .0265077{col 79}{space 3} .8330239
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1212041{col 38}{space 2}  .162678{col 49}{space 1}   -0.75{col 58}{space 3}0.456{col 66}{space 4} -.440047{col 79}{space 3} .1976388
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0013838{col 38}{space 2} .0021384{col 49}{space 1}   -0.65{col 58}{space 3}0.518{col 66}{space 4}-.0055751{col 79}{space 3} .0028074
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.2127634{col 38}{space 2}  .052425{col 49}{space 1}   -4.06{col 58}{space 3}0.000{col 66}{space 4}-.3155144{col 79}{space 3}-.1100124
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0075019{col 38}{space 2} .0024441{col 49}{space 1}    3.07{col 58}{space 3}0.002{col 66}{space 4} .0027115{col 79}{space 3} .0122922
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2} -.000083{col 38}{space 2} .0000275{col 49}{space 1}   -3.02{col 58}{space 3}0.003{col 66}{space 4} -.000137{col 79}{space 3}-.0000291
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.6987198{col 38}{space 2} .6059898{col 49}{space 1}   -1.15{col 58}{space 3}0.249{col 66}{space 4}-1.886438{col 79}{space 3} .4889983
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 95 failures and 0 successes completely determined.{p_end}

{com}. estimates store p6
{txt}
{com}. 
. * Model 7 (change in demands trichotomous)
. logit neg_DV ib0.battles_escalation i.change_in_demands_tri str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -425.8587}  
Iteration 1:{space 3}log pseudolikelihood = {res:-381.16415}  
Iteration 2:{space 3}log pseudolikelihood = {res:-370.33798}  
Iteration 3:{space 3}log pseudolikelihood = {res:-369.20455}  
Iteration 4:{space 3}log pseudolikelihood = {res:-368.95338}  
Iteration 5:{space 3}log pseudolikelihood = {res:-368.66771}  
Iteration 6:{space 3}log pseudolikelihood = {res:-367.84489}  
Iteration 7:{space 3}log pseudolikelihood = {res:-366.65466}  
Iteration 8:{space 3}log pseudolikelihood = {res:-366.00924}  
Iteration 9:{space 3}log pseudolikelihood = {res: -365.9343}  
Iteration 10:{space 2}log pseudolikelihood = {res:-365.93341}  
Iteration 11:{space 2}log pseudolikelihood = {res:-365.93341}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,310
{txt}{col 49}Wald chi2({res}11{txt}){col 67}= {res}     99.36
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-365.93341{txt}{col 49}Pseudo R2{col 67}= {res}    0.1407

{txt}{ralign 87:(Std. Err. adjusted for {res:40} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.6138293{col 35}{space 2} .2604243{col 46}{space 1}   -2.36{col 55}{space 3}0.018{col 63}{space 4}-1.124251{col 76}{space 3}-.1034071
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.3377303{col 35}{space 2} .1731812{col 46}{space 1}   -1.95{col 55}{space 3}0.051{col 63}{space 4}-.6771593{col 76}{space 3} .0016986
{txt}{space 21} {c |}
change_in_demands_tri {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.5625121{col 35}{space 2} .2195173{col 46}{space 1}   -2.56{col 55}{space 3}0.010{col 63}{space 4} -.992758{col 76}{space 3}-.1322661
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.0286789{col 35}{space 2} .2472392{col 46}{space 1}   -0.12{col 55}{space 3}0.908{col 63}{space 4}-.5132589{col 76}{space 3}  .455901
{txt}{space 21} {c |}
{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0079882{col 35}{space 2} .1377531{col 46}{space 1}    0.06{col 55}{space 3}0.954{col 63}{space 4}-.2620029{col 76}{space 3} .2779793
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5736538{col 35}{space 2} .1907234{col 46}{space 1}    3.01{col 55}{space 3}0.003{col 63}{space 4} .1998428{col 76}{space 3} .9474647
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0628983{col 35}{space 2} .1616809{col 46}{space 1}    0.39{col 55}{space 3}0.697{col 63}{space 4}-.2539905{col 76}{space 3}  .379787
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0011155{col 35}{space 2} .0027451{col 46}{space 1}   -0.41{col 55}{space 3}0.684{col 63}{space 4}-.0064958{col 76}{space 3} .0042647
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.2350737{col 35}{space 2} .0544235{col 46}{space 1}   -4.32{col 55}{space 3}0.000{col 63}{space 4}-.3417417{col 76}{space 3}-.1284056
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0098003{col 35}{space 2} .0034613{col 46}{space 1}    2.83{col 55}{space 3}0.005{col 63}{space 4} .0030162{col 76}{space 3} .0165844
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0001344{col 35}{space 2} .0000572{col 46}{space 1}   -2.35{col 55}{space 3}0.019{col 63}{space 4}-.0002465{col 76}{space 3}-.0000224
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.5037955{col 35}{space 2} .5523105{col 46}{space 1}   -0.91{col 55}{space 3}0.362{col 63}{space 4}-1.586304{col 76}{space 3} .5787131
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 162 failures and 0 successes completely determined.{p_end}

{com}. estimates store p7
{txt}
{com}. 
. * Model 8 (change in demands trichotomous & third party involvement)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement i.change_in_demands_tri str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-328.90718}  
Iteration 1:{space 3}log pseudolikelihood = {res:-285.84687}  
Iteration 2:{space 3}log pseudolikelihood = {res:-271.15846}  
Iteration 3:{space 3}log pseudolikelihood = {res:-269.91834}  
Iteration 4:{space 3}log pseudolikelihood = {res:-269.68624}  
Iteration 5:{space 3}log pseudolikelihood = {res:-269.50483}  
Iteration 6:{space 3}log pseudolikelihood = {res:-269.04544}  
Iteration 7:{space 3}log pseudolikelihood = {res:-268.54534}  
Iteration 8:{space 3}log pseudolikelihood = {res:-268.43178}  
Iteration 9:{space 3}log pseudolikelihood = {res:-268.42651}  
Iteration 10:{space 2}log pseudolikelihood = {res: -268.4265}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,037
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    288.23
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -268.4265{txt}{col 49}Pseudo R2{col 67}= {res}    0.1839

{txt}{ralign 90:(Std. Err. adjusted for {res:33} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.6978753{col 38}{space 2} .5411021{col 49}{space 1}   -1.29{col 58}{space 3}0.197{col 66}{space 4}-1.758416{col 79}{space 3} .3626653
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.211717{col 38}{space 2}  .445022{col 49}{space 1}   -2.72{col 58}{space 3}0.006{col 66}{space 4}-2.083944{col 79}{space 3}-.3394896
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .4993291{col 38}{space 2} .5554312{col 49}{space 1}    0.90{col 58}{space 3}0.369{col 66}{space 4}-.5892961{col 79}{space 3} 1.587954
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}  .675425{col 38}{space 2} .7047679{col 49}{space 1}    0.96{col 58}{space 3}0.338{col 66}{space 4}-.7058947{col 79}{space 3} 2.056745
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.003116{col 38}{space 2} .5682954{col 49}{space 1}    1.77{col 58}{space 3}0.078{col 66}{space 4}-.1107222{col 79}{space 3} 2.116955
{txt}{space 24} {c |}
{space 3}change_in_demands_tri {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-1.183571{col 38}{space 2} .2629704{col 49}{space 1}   -4.50{col 58}{space 3}0.000{col 66}{space 4}-1.698983{col 79}{space 3}-.6681585
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.7802954{col 38}{space 2} .3179996{col 49}{space 1}   -2.45{col 58}{space 3}0.014{col 66}{space 4}-1.403563{col 79}{space 3}-.1570276
{txt}{space 24} {c |}
{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.1256744{col 38}{space 2}  .136472{col 49}{space 1}   -0.92{col 58}{space 3}0.357{col 66}{space 4}-.3931546{col 79}{space 3} .1418057
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5242161{col 38}{space 2} .2744176{col 49}{space 1}    1.91{col 58}{space 3}0.056{col 66}{space 4}-.0136325{col 79}{space 3} 1.062065
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0461759{col 38}{space 2} .1878803{col 49}{space 1}   -0.25{col 58}{space 3}0.806{col 66}{space 4}-.4144144{col 79}{space 3} .3220627
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0017905{col 38}{space 2} .0021863{col 49}{space 1}   -0.82{col 58}{space 3}0.413{col 66}{space 4}-.0060756{col 79}{space 3} .0024946
{txt}{space 23}t {c |}{col 26}{res}{space 2} -.169082{col 38}{space 2} .0642896{col 49}{space 1}   -2.63{col 58}{space 3}0.009{col 66}{space 4}-.2950874{col 79}{space 3}-.0430767
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0063947{col 38}{space 2} .0035894{col 49}{space 1}    1.78{col 58}{space 3}0.075{col 66}{space 4}-.0006404{col 79}{space 3} .0134299
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000833{col 38}{space 2} .0000502{col 49}{space 1}   -1.66{col 58}{space 3}0.097{col 66}{space 4}-.0001818{col 79}{space 3} .0000152
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} .0607019{col 38}{space 2} .6742369{col 49}{space 1}    0.09{col 58}{space 3}0.928{col 66}{space 4}-1.260778{col 79}{space 3} 1.382182
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 101 failures and 0 successes completely determined.{p_end}

{com}. estimates store p8
{txt}
{com}. 
. * Model 9 (concessions/demands during last negotiations)
. logit neg_DV ib0.battles_escalation ib1.con_rate_4cat str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-436.84516}  
Iteration 1:{space 3}log pseudolikelihood = {res:-391.64483}  
Iteration 2:{space 3}log pseudolikelihood = {res:-380.95138}  
Iteration 3:{space 3}log pseudolikelihood = {res:-379.85719}  
Iteration 4:{space 3}log pseudolikelihood = {res:-379.58956}  
Iteration 5:{space 3}log pseudolikelihood = {res:-379.23857}  
Iteration 6:{space 3}log pseudolikelihood = {res:-378.24685}  
Iteration 7:{space 3}log pseudolikelihood = {res:  -376.737}  
Iteration 8:{space 3}log pseudolikelihood = {res:-375.99747}  
Iteration 9:{space 3}log pseudolikelihood = {res:-375.89076}  
Iteration 10:{space 2}log pseudolikelihood = {res:-375.88871}  
Iteration 11:{space 2}log pseudolikelihood = {res:-375.88871}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,331
{txt}{col 49}Wald chi2({res}12{txt}){col 67}= {res}    134.51
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-375.88871{txt}{col 49}Pseudo R2{col 67}= {res}    0.1395

{txt}{ralign 84:(Std. Err. adjusted for {res:40} clusters in dyadid)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            neg_DV{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
battles_escalation {c |}
{space 16}1  {c |}{col 20}{res}{space 2} -.469571{col 32}{space 2} .2574231{col 43}{space 1}   -1.82{col 52}{space 3}0.068{col 60}{space 4}-.9741111{col 73}{space 3} .0349691
{txt}{space 16}2  {c |}{col 20}{res}{space 2}-.3055393{col 32}{space 2} .1720184{col 43}{space 1}   -1.78{col 52}{space 3}0.076{col 60}{space 4}-.6426891{col 73}{space 3} .0316105
{txt}{space 18} {c |}
{space 5}con_rate_4cat {c |}
{space 16}0  {c |}{col 20}{res}{space 2}-.3471638{col 32}{space 2} .2064971{col 43}{space 1}   -1.68{col 52}{space 3}0.093{col 60}{space 4}-.7518908{col 73}{space 3} .0575631
{txt}{space 16}2  {c |}{col 20}{res}{space 2} .3023926{col 32}{space 2} .1690343{col 43}{space 1}    1.79{col 52}{space 3}0.074{col 60}{space 4}-.0289085{col 73}{space 3} .6336937
{txt}{space 16}3  {c |}{col 20}{res}{space 2} .1807355{col 32}{space 2} .4842596{col 43}{space 1}    0.37{col 52}{space 3}0.709{col 60}{space 4}-.7683959{col 73}{space 3} 1.129867
{txt}{space 18} {c |}
{space 6}str_centcomm {c |}{col 20}{res}{space 2} .0409151{col 32}{space 2} .1220036{col 43}{space 1}    0.34{col 52}{space 3}0.737{col 60}{space 4}-.1982076{col 73}{space 3} .2800377
{txt}{space 12}parity {c |}{col 20}{res}{space 2} .4038692{col 32}{space 2} .1943337{col 43}{space 1}    2.08{col 52}{space 3}0.038{col 60}{space 4} .0229822{col 73}{space 3} .7847563
{txt}{space 10}groupnum {c |}{col 20}{res}{space 2} .0073054{col 32}{space 2} .1459223{col 43}{space 1}    0.05{col 52}{space 3}0.960{col 60}{space 4}-.2786971{col 73}{space 3}  .293308
{txt}{space 7}dyad_months {c |}{col 20}{res}{space 2}-.0010692{col 32}{space 2} .0025511{col 43}{space 1}   -0.42{col 52}{space 3}0.675{col 60}{space 4}-.0060692{col 73}{space 3} .0039308
{txt}{space 17}t {c |}{col 20}{res}{space 2}-.2570295{col 32}{space 2} .0584097{col 43}{space 1}   -4.40{col 52}{space 3}0.000{col 60}{space 4}-.3715104{col 73}{space 3}-.1425486
{txt}{space 18} {c |}
{space 11}c.t#c.t {c |}{col 20}{res}{space 2} .0110958{col 32}{space 2} .0038008{col 43}{space 1}    2.92{col 52}{space 3}0.004{col 60}{space 4} .0036463{col 73}{space 3} .0185453
{txt}{space 18} {c |}
{space 7}c.t#c.t#c.t {c |}{col 20}{res}{space 2}-.0001538{col 32}{space 2} .0000657{col 43}{space 1}   -2.34{col 52}{space 3}0.019{col 60}{space 4}-.0002826{col 73}{space 3} -.000025
{txt}{space 18} {c |}
{space 13}_cons {c |}{col 20}{res}{space 2}-.6086403{col 32}{space 2} .5155092{col 43}{space 1}   -1.18{col 52}{space 3}0.238{col 60}{space 4} -1.61902{col 73}{space 3} .4017392
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 166 failures and 0 successes completely determined.{p_end}

{com}. estimates store p9
{txt}
{com}. 
. * Model 10 (concessions/demands during last negotiations)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement ib1.con_rate_4cat str_centcomm parity groupnum dyad_months c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-338.92578}  
Iteration 1:{space 3}log pseudolikelihood = {res:-295.65391}  
Iteration 2:{space 3}log pseudolikelihood = {res:-283.22043}  
Iteration 3:{space 3}log pseudolikelihood = {res:-282.27203}  
Iteration 4:{space 3}log pseudolikelihood = {res:-281.95431}  
Iteration 5:{space 3}log pseudolikelihood = {res:-281.48359}  
Iteration 6:{space 3}log pseudolikelihood = {res:-280.43044}  
Iteration 7:{space 3}log pseudolikelihood = {res:-279.51124}  
Iteration 8:{space 3}log pseudolikelihood = {res:-279.24226}  
Iteration 9:{space 3}log pseudolikelihood = {res:-279.22544}  
Iteration 10:{space 2}log pseudolikelihood = {res:-279.22542}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,048
{txt}{col 49}Wald chi2({res}15{txt}){col 67}= {res}    202.04
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-279.22542{txt}{col 49}Pseudo R2{col 67}= {res}    0.1761

{txt}{ralign 90:(Std. Err. adjusted for {res:33} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.4237091{col 38}{space 2} .5555737{col 49}{space 1}   -0.76{col 58}{space 3}0.446{col 66}{space 4}-1.512614{col 79}{space 3} .6651954
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.002277{col 38}{space 2} .4613772{col 49}{space 1}   -2.17{col 58}{space 3}0.030{col 66}{space 4} -1.90656{col 79}{space 3}-.0979945
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .5358199{col 38}{space 2} .5656727{col 49}{space 1}    0.95{col 58}{space 3}0.344{col 66}{space 4}-.5728783{col 79}{space 3} 1.644518
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .3853426{col 38}{space 2} .7387241{col 49}{space 1}    0.52{col 58}{space 3}0.602{col 66}{space 4} -1.06253{col 79}{space 3} 1.833215
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.096695{col 38}{space 2} .5849635{col 49}{space 1}    1.87{col 58}{space 3}0.061{col 66}{space 4}-.0498122{col 79}{space 3} 2.243202
{txt}{space 24} {c |}
{space 11}con_rate_4cat {c |}
{space 22}0  {c |}{col 26}{res}{space 2}-.4040938{col 38}{space 2} .2836383{col 49}{space 1}   -1.42{col 58}{space 3}0.154{col 66}{space 4}-.9600146{col 79}{space 3}  .151827
{txt}{space 22}2  {c |}{col 26}{res}{space 2} .3023317{col 38}{space 2} .1848459{col 49}{space 1}    1.64{col 58}{space 3}0.102{col 66}{space 4}-.0599596{col 79}{space 3}  .664623
{txt}{space 22}3  {c |}{col 26}{res}{space 2} .1132845{col 38}{space 2}  .518199{col 49}{space 1}    0.22{col 58}{space 3}0.827{col 66}{space 4}-.9023669{col 79}{space 3} 1.128936
{txt}{space 24} {c |}
{space 12}str_centcomm {c |}{col 26}{res}{space 2} -.093497{col 38}{space 2} .1149847{col 49}{space 1}   -0.81{col 58}{space 3}0.416{col 66}{space 4}-.3188628{col 79}{space 3} .1318687
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .4474913{col 38}{space 2}  .201373{col 49}{space 1}    2.22{col 58}{space 3}0.026{col 66}{space 4} .0528075{col 79}{space 3} .8421751
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.1525565{col 38}{space 2} .1694835{col 49}{space 1}   -0.90{col 58}{space 3}0.368{col 66}{space 4} -.484738{col 79}{space 3}  .179625
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2} -.001774{col 38}{space 2} .0022278{col 49}{space 1}   -0.80{col 58}{space 3}0.426{col 66}{space 4}-.0061404{col 79}{space 3} .0025925
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.2339968{col 38}{space 2} .0619061{col 49}{space 1}   -3.78{col 58}{space 3}0.000{col 66}{space 4}-.3553306{col 79}{space 3} -.112663
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0098664{col 38}{space 2} .0037055{col 49}{space 1}    2.66{col 58}{space 3}0.008{col 66}{space 4} .0026038{col 79}{space 3}  .017129
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0001283{col 38}{space 2} .0000564{col 49}{space 1}   -2.28{col 58}{space 3}0.023{col 66}{space 4}-.0002389{col 79}{space 3}-.0000178
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.5084609{col 38}{space 2} .6638104{col 49}{space 1}   -0.77{col 58}{space 3}0.444{col 66}{space 4}-1.809505{col 79}{space 3} .7925836
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 109 failures and 0 successes completely determined.{p_end}

{com}. estimates store p10
{txt}
{com}. 
. * Table A5 *
. #delimit;
{txt}delimiter now ;
{com}. esttab p1 p2 p3 p4 p5 p6 p7 p8 p9 p10 using tableA5.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A5: Alternative Political Outcome of Negotiations Measures) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. +p<0.1, *p<0.05.) 
>   nonumbers mtitles("Strong Concessions" " " "Political Concessions" " " "Num of Concessions" " " "Change in Demands" " " "Concessions/Demands" " ")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA5.rtf not found)
(output written to {browse  `"tableA5.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
.         
.         
. *************************************
. *************************************
. *** Table A6: Additional Controls ***
. *************************************
. *************************************
. tsset dyadid date
{res}{txt}{col 8}panel variable:  {res}dyadid (unbalanced)
{txt}{col 9}time variable:  {res}{col 25}date, 348 to 599
{txt}{col 17}delta:  {res}1 unit
{txt}
{com}. gen totaldeaths_month_thous=totaldeaths_monthtotal/1000
{txt}(4 missing values generated)

{com}. 
. * Model 1 (Additional controls for conflict intensity)          
. logit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months l.totaldeaths_month_thous l.count_state_success avg_battles_during_neg_nodecay c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-461.60119}  
Iteration 1:{space 3}log pseudolikelihood = {res:-414.35062}  
Iteration 2:{space 3}log pseudolikelihood = {res:-402.70468}  
Iteration 3:{space 3}log pseudolikelihood = {res:-401.62887}  
Iteration 4:{space 3}log pseudolikelihood = {res:-401.14906}  
Iteration 5:{space 3}log pseudolikelihood = {res:-400.41987}  
Iteration 6:{space 3}log pseudolikelihood = {res:-399.39305}  
Iteration 7:{space 3}log pseudolikelihood = {res:-398.79764}  
Iteration 8:{space 3}log pseudolikelihood = {res:-398.72025}  
Iteration 9:{space 3}log pseudolikelihood = {res:-398.71926}  
Iteration 10:{space 2}log pseudolikelihood = {res:-398.71926}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,463
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    146.60
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-398.71926{txt}{col 49}Pseudo R2{col 67}= {res}    0.1362

{txt}{ralign 92:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2}  -.41088{col 40}{space 2} .2625627{col 51}{space 1}   -1.56{col 60}{space 3}0.118{col 68}{space 4}-.9254933{col 81}{space 3} .1037334
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-.4535251{col 40}{space 2} .1706445{col 51}{space 1}   -2.66{col 60}{space 3}0.008{col 68}{space 4}-.7879821{col 81}{space 3} -.119068
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4394584{col 40}{space 2} .1627471{col 51}{space 1}    2.70{col 60}{space 3}0.007{col 68}{space 4} .1204801{col 81}{space 3} .7584368
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.0033099{col 40}{space 2} .1154841{col 51}{space 1}   -0.03{col 60}{space 3}0.977{col 68}{space 4}-.2296547{col 81}{space 3} .2230348
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .5215657{col 40}{space 2} .1764751{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} .1756809{col 81}{space 3} .8674505
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2} .0732986{col 40}{space 2} .1598475{col 51}{space 1}    0.46{col 60}{space 3}0.647{col 68}{space 4}-.2399968{col 81}{space 3} .3865939
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0018042{col 40}{space 2} .0027657{col 51}{space 1}   -0.65{col 60}{space 3}0.514{col 68}{space 4}-.0072249{col 81}{space 3} .0036165
{txt}{space 26} {c |}
{space 3}totaldeaths_month_thous {c |}
{space 23}L1. {c |}{col 28}{res}{space 2}-.3481996{col 40}{space 2} .2312949{col 51}{space 1}   -1.51{col 60}{space 3}0.132{col 68}{space 4}-.8015292{col 81}{space 3} .1051301
{txt}{space 26} {c |}
{space 7}count_state_success {c |}
{space 23}L1. {c |}{col 28}{res}{space 2}-.0968109{col 40}{space 2} .2573163{col 51}{space 1}   -0.38{col 60}{space 3}0.707{col 68}{space 4}-.6011416{col 81}{space 3} .4075198
{txt}{space 26} {c |}
avg_battles_during_neg_n~y {c |}{col 28}{res}{space 2} .0053435{col 40}{space 2} .0140831{col 51}{space 1}    0.38{col 60}{space 3}0.704{col 68}{space 4}-.0222588{col 81}{space 3} .0329458
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1875752{col 40}{space 2} .0449441{col 51}{space 1}   -4.17{col 60}{space 3}0.000{col 68}{space 4} -.275664{col 81}{space 3}-.0994863
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0056499{col 40}{space 2} .0022974{col 51}{space 1}    2.46{col 60}{space 3}0.014{col 68}{space 4} .0011471{col 81}{space 3} .0101528
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000567{col 40}{space 2} .0000282{col 51}{space 1}   -2.01{col 60}{space 3}0.044{col 68}{space 4}-.0001119{col 81}{space 3}-1.44e-06
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-.8735249{col 40}{space 2} .4868941{col 51}{space 1}   -1.79{col 60}{space 3}0.073{col 68}{space 4} -1.82782{col 81}{space 3} .0807699
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 112 failures and 0 successes completely determined.{p_end}

{com}. estimates store a18
{txt}
{com}. 
. * Model 2 (Additional conflict intensity controls & third-party involvement)
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months l.totaldeaths_month_thous l.count_state_success avg_battles_during_neg_nodecay c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-361.57531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-315.09598}  
Iteration 2:{space 3}log pseudolikelihood = {res:-301.00344}  
Iteration 3:{space 3}log pseudolikelihood = {res:-299.67551}  
Iteration 4:{space 3}log pseudolikelihood = {res: -299.0673}  
Iteration 5:{space 3}log pseudolikelihood = {res:-298.28721}  
Iteration 6:{space 3}log pseudolikelihood = {res:-297.57336}  
Iteration 7:{space 3}log pseudolikelihood = {res:-297.37053}  
Iteration 8:{space 3}log pseudolikelihood = {res:-297.36046}  
Iteration 9:{space 3}log pseudolikelihood = {res:-297.36044}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,161
{txt}{col 49}Wald chi2({res}16{txt}){col 67}= {res}    185.45
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-297.36044{txt}{col 49}Pseudo R2{col 67}= {res}    0.1776

{txt}{ralign 92:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                    neg_DV{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}battles_escalation {c |}
{space 24}1  {c |}{col 28}{res}{space 2} -.364831{col 40}{space 2} .4857332{col 51}{space 1}   -0.75{col 60}{space 3}0.453{col 68}{space 4} -1.31685{col 81}{space 3} .5871885
{txt}{space 24}2  {c |}{col 28}{res}{space 2}-1.256381{col 40}{space 2} .4191027{col 51}{space 1}   -3.00{col 60}{space 3}0.003{col 68}{space 4}-2.077807{col 81}{space 3}-.4349546
{txt}{space 26} {c |}
{space 2}1.thirdparty_involvement {c |}{col 28}{res}{space 2} .6448317{col 40}{space 2} .5681282{col 51}{space 1}    1.14{col 60}{space 3}0.256{col 68}{space 4}-.4686791{col 81}{space 3} 1.758343
{txt}{space 26} {c |}
{space 8}battles_escalation#{c |}
{space 4}thirdparty_involvement {c |}
{space 22}1 1  {c |}{col 28}{res}{space 2} .2597939{col 40}{space 2} .6939717{col 51}{space 1}    0.37{col 60}{space 3}0.708{col 68}{space 4}-1.100366{col 81}{space 3} 1.619953
{txt}{space 22}2 1  {c |}{col 28}{res}{space 2}  1.19593{col 40}{space 2} .5085421{col 51}{space 1}    2.35{col 60}{space 3}0.019{col 68}{space 4} .1992061{col 81}{space 3} 2.192655
{txt}{space 26} {c |}
{space 5}weak_con_last_neg_dum {c |}{col 28}{res}{space 2} .4389062{col 40}{space 2}  .215047{col 51}{space 1}    2.04{col 60}{space 3}0.041{col 68}{space 4} .0174219{col 81}{space 3} .8603904
{txt}{space 14}str_centcomm {c |}{col 28}{res}{space 2}-.1241768{col 40}{space 2} .0979606{col 51}{space 1}   -1.27{col 60}{space 3}0.205{col 68}{space 4} -.316176{col 81}{space 3} .0678224
{txt}{space 20}parity {c |}{col 28}{res}{space 2} .5463172{col 40}{space 2} .2394149{col 51}{space 1}    2.28{col 60}{space 3}0.022{col 68}{space 4} .0770726{col 81}{space 3} 1.015562
{txt}{space 18}groupnum {c |}{col 28}{res}{space 2}-.1432936{col 40}{space 2} .1767074{col 51}{space 1}   -0.81{col 60}{space 3}0.417{col 68}{space 4}-.4896338{col 81}{space 3} .2030466
{txt}{space 15}dyad_months {c |}{col 28}{res}{space 2}-.0012226{col 40}{space 2} .0022305{col 51}{space 1}   -0.55{col 60}{space 3}0.584{col 68}{space 4}-.0055943{col 81}{space 3}  .003149
{txt}{space 26} {c |}
{space 3}totaldeaths_month_thous {c |}
{space 23}L1. {c |}{col 28}{res}{space 2} .6808347{col 40}{space 2} .4437719{col 51}{space 1}    1.53{col 60}{space 3}0.125{col 68}{space 4}-.1889423{col 81}{space 3} 1.550612
{txt}{space 26} {c |}
{space 7}count_state_success {c |}
{space 23}L1. {c |}{col 28}{res}{space 2}-.6682324{col 40}{space 2} .5393188{col 51}{space 1}   -1.24{col 60}{space 3}0.215{col 68}{space 4}-1.725278{col 81}{space 3}  .388813
{txt}{space 26} {c |}
avg_battles_during_neg_n~y {c |}{col 28}{res}{space 2}-.0155072{col 40}{space 2} .0205448{col 51}{space 1}   -0.75{col 60}{space 3}0.450{col 68}{space 4}-.0557742{col 81}{space 3} .0247599
{txt}{space 25}t {c |}{col 28}{res}{space 2}-.1691953{col 40}{space 2} .0495193{col 51}{space 1}   -3.42{col 60}{space 3}0.001{col 68}{space 4}-.2662513{col 81}{space 3}-.0721393
{txt}{space 26} {c |}
{space 19}c.t#c.t {c |}{col 28}{res}{space 2} .0053366{col 40}{space 2} .0024065{col 51}{space 1}    2.22{col 60}{space 3}0.027{col 68}{space 4} .0006199{col 81}{space 3} .0100533
{txt}{space 26} {c |}
{space 15}c.t#c.t#c.t {c |}{col 28}{res}{space 2}-.0000525{col 40}{space 2} .0000274{col 51}{space 1}   -1.91{col 60}{space 3}0.056{col 68}{space 4}-.0001062{col 81}{space 3} 1.28e-06
{txt}{space 26} {c |}
{space 21}_cons {c |}{col 28}{res}{space 2}-.8252084{col 40}{space 2} .5973327{col 51}{space 1}   -1.38{col 60}{space 3}0.167{col 68}{space 4}-1.995959{col 81}{space 3} .3455422
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 81 failures and 0 successes completely determined.{p_end}

{com}. estimates store a19
{txt}
{com}. 
. * Model 3 (Additional group/conflict characteristic controls)   
. logit neg_DV ib0.battles_escalation thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months territory reb_polwing rebelsupport_dum govsupport_dum dem_dummy_nona ib3.indirect_learning_4cat c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-335.15699}  
Iteration 1:{space 3}log pseudolikelihood = {res: -290.1958}  
Iteration 2:{space 3}log pseudolikelihood = {res:-274.89495}  
Iteration 3:{space 3}log pseudolikelihood = {res:-273.57203}  
Iteration 4:{space 3}log pseudolikelihood = {res:-273.00079}  
Iteration 5:{space 3}log pseudolikelihood = {res: -272.4677}  
Iteration 6:{space 3}log pseudolikelihood = {res:-271.91324}  
Iteration 7:{space 3}log pseudolikelihood = {res:-271.75986}  
Iteration 8:{space 3}log pseudolikelihood = {res: -271.7545}  
Iteration 9:{space 3}log pseudolikelihood = {res: -271.7545}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,077
{txt}{col 49}Wald chi2({res}19{txt}){col 67}= {res}   1283.45
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -271.7545{txt}{col 49}Pseudo R2{col 67}= {res}    0.1892

{txt}{ralign 88:(Std. Err. adjusted for {res:30} clusters in dyadid)}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}                neg_DV{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}battles_escalation {c |}
{space 20}1  {c |}{col 24}{res}{space 2}-.1473878{col 36}{space 2} .2363891{col 47}{space 1}   -0.62{col 56}{space 3}0.533{col 64}{space 4}-.6107019{col 77}{space 3} .3159264
{txt}{space 20}2  {c |}{col 24}{res}{space 2}-.4141981{col 36}{space 2} .2089831{col 47}{space 1}   -1.98{col 56}{space 3}0.047{col 64}{space 4}-.8237976{col 77}{space 3}-.0045987
{txt}{space 22} {c |}
thirdparty_involvement {c |}{col 24}{res}{space 2} 1.211045{col 36}{space 2} .2879474{col 47}{space 1}    4.21{col 56}{space 3}0.000{col 64}{space 4} .6466785{col 77}{space 3} 1.775412
{txt}{space 1}weak_con_last_neg_dum {c |}{col 24}{res}{space 2} .4281171{col 36}{space 2} .2849008{col 47}{space 1}    1.50{col 56}{space 3}0.133{col 64}{space 4}-.1302782{col 77}{space 3} .9865124
{txt}{space 10}str_centcomm {c |}{col 24}{res}{space 2}-.0715988{col 36}{space 2} .1744714{col 47}{space 1}   -0.41{col 56}{space 3}0.682{col 64}{space 4}-.4135564{col 77}{space 3} .2703588
{txt}{space 16}parity {c |}{col 24}{res}{space 2} .5149929{col 36}{space 2} .2852791{col 47}{space 1}    1.81{col 56}{space 3}0.071{col 64}{space 4}-.0441438{col 77}{space 3}  1.07413
{txt}{space 14}groupnum {c |}{col 24}{res}{space 2} .1377976{col 36}{space 2} .1511376{col 47}{space 1}    0.91{col 56}{space 3}0.362{col 64}{space 4}-.1584267{col 77}{space 3} .4340219
{txt}{space 11}dyad_months {c |}{col 24}{res}{space 2} .0006303{col 36}{space 2} .0017856{col 47}{space 1}    0.35{col 56}{space 3}0.724{col 64}{space 4}-.0028694{col 77}{space 3}   .00413
{txt}{space 13}territory {c |}{col 24}{res}{space 2}-2.037706{col 36}{space 2} .8233856{col 47}{space 1}   -2.47{col 56}{space 3}0.013{col 64}{space 4}-3.651512{col 77}{space 3}-.4238999
{txt}{space 11}reb_polwing {c |}{col 24}{res}{space 2}-.1914814{col 36}{space 2}   .34545{col 47}{space 1}   -0.55{col 56}{space 3}0.579{col 64}{space 4}-.8685509{col 77}{space 3} .4855881
{txt}{space 6}rebelsupport_dum {c |}{col 24}{res}{space 2} -.937062{col 36}{space 2} .4413396{col 47}{space 1}   -2.12{col 56}{space 3}0.034{col 64}{space 4}-1.802072{col 77}{space 3}-.0720523
{txt}{space 8}govsupport_dum {c |}{col 24}{res}{space 2} .9870942{col 36}{space 2} .4400255{col 47}{space 1}    2.24{col 56}{space 3}0.025{col 64}{space 4} .1246602{col 77}{space 3} 1.849528
{txt}{space 8}dem_dummy_nona {c |}{col 24}{res}{space 2} 1.584599{col 36}{space 2} .4093094{col 47}{space 1}    3.87{col 56}{space 3}0.000{col 64}{space 4} .7823675{col 77}{space 3} 2.386831
{txt}{space 22} {c |}
indirect_learning_4cat {c |}
{space 20}0  {c |}{col 24}{res}{space 2}-.6334761{col 36}{space 2} .3242614{col 47}{space 1}   -1.95{col 56}{space 3}0.051{col 64}{space 4}-1.269017{col 77}{space 3} .0020645
{txt}{space 20}1  {c |}{col 24}{res}{space 2} .0632841{col 36}{space 2} .5933948{col 47}{space 1}    0.11{col 56}{space 3}0.915{col 64}{space 4}-1.099748{col 77}{space 3} 1.226316
{txt}{space 20}2  {c |}{col 24}{res}{space 2} .0502144{col 36}{space 2} .2994096{col 47}{space 1}    0.17{col 56}{space 3}0.867{col 64}{space 4}-.5366175{col 77}{space 3} .6370464
{txt}{space 22} {c |}
{space 21}t {c |}{col 24}{res}{space 2}-.1571636{col 36}{space 2} .0663418{col 47}{space 1}   -2.37{col 56}{space 3}0.018{col 64}{space 4}-.2871912{col 77}{space 3}-.0271361
{txt}{space 22} {c |}
{space 15}c.t#c.t {c |}{col 24}{res}{space 2} .0053971{col 36}{space 2}  .002939{col 47}{space 1}    1.84{col 56}{space 3}0.066{col 64}{space 4}-.0003631{col 77}{space 3} .0111574
{txt}{space 22} {c |}
{space 11}c.t#c.t#c.t {c |}{col 24}{res}{space 2}-.0000524{col 36}{space 2} .0000329{col 47}{space 1}   -1.59{col 56}{space 3}0.111{col 64}{space 4} -.000117{col 77}{space 3} .0000121
{txt}{space 22} {c |}
{space 17}_cons {c |}{col 24}{res}{space 2}-1.952044{col 36}{space 2} .6935573{col 47}{space 1}   -2.81{col 56}{space 3}0.005{col 64}{space 4}-3.311391{col 77}{space 3}-.5926963
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 82 failures and 0 successes completely determined.{p_end}

{com}. estimates store a20
{txt}
{com}. 
. * Model 4 (Additional group/conflict characteristic controls & third-party involvement) 
. logit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months territory reb_polwing rebelsupport_dum govsupport_dum dem_dummy_nona ib3.indirect_learning_4cat c.t##c.t##c.t , cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-335.15699}  
Iteration 1:{space 3}log pseudolikelihood = {res:-288.74631}  
Iteration 2:{space 3}log pseudolikelihood = {res:-272.69445}  
Iteration 3:{space 3}log pseudolikelihood = {res:-271.41803}  
Iteration 4:{space 3}log pseudolikelihood = {res:-270.84697}  
Iteration 5:{space 3}log pseudolikelihood = {res:-270.34836}  
Iteration 6:{space 3}log pseudolikelihood = {res:-269.88221}  
Iteration 7:{space 3}log pseudolikelihood = {res:-269.75919}  
Iteration 8:{space 3}log pseudolikelihood = {res:-269.75484}  
Iteration 9:{space 3}log pseudolikelihood = {res:-269.75484}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,077
{txt}{col 49}Wald chi2({res}21{txt}){col 67}= {res}   1751.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-269.75484{txt}{col 49}Pseudo R2{col 67}= {res}    0.1951

{txt}{ralign 90:(Std. Err. adjusted for {res:30} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.3054779{col 38}{space 2} .3706185{col 49}{space 1}   -0.82{col 58}{space 3}0.410{col 66}{space 4}-1.031877{col 79}{space 3}  .420921
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.336712{col 38}{space 2} .5015812{col 49}{space 1}   -2.66{col 58}{space 3}0.008{col 66}{space 4}-2.319793{col 79}{space 3}-.3536304
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .6879746{col 38}{space 2} .4738379{col 49}{space 1}    1.45{col 58}{space 3}0.147{col 66}{space 4}-.2407306{col 79}{space 3}  1.61668
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .1519728{col 38}{space 2} .6395882{col 49}{space 1}    0.24{col 58}{space 3}0.812{col 66}{space 4}-1.101597{col 79}{space 3} 1.405543
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.199266{col 38}{space 2} .5893565{col 49}{space 1}    2.03{col 58}{space 3}0.042{col 66}{space 4}  .044149{col 79}{space 3} 2.354384
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .3323357{col 38}{space 2} .3225956{col 49}{space 1}    1.03{col 58}{space 3}0.303{col 66}{space 4}  -.29994{col 79}{space 3} .9646114
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0495792{col 38}{space 2} .1820459{col 49}{space 1}   -0.27{col 58}{space 3}0.785{col 66}{space 4}-.4063825{col 79}{space 3} .3072242
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5507369{col 38}{space 2} .3082785{col 49}{space 1}    1.79{col 58}{space 3}0.074{col 66}{space 4}-.0534779{col 79}{space 3} 1.154952
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2} .0556682{col 38}{space 2} .1823991{col 49}{space 1}    0.31{col 58}{space 3}0.760{col 66}{space 4}-.3018276{col 79}{space 3} .4131639
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2} .0001193{col 38}{space 2} .0019648{col 49}{space 1}    0.06{col 58}{space 3}0.952{col 66}{space 4}-.0037316{col 79}{space 3} .0039702
{txt}{space 15}territory {c |}{col 26}{res}{space 2}-2.230554{col 38}{space 2} .8566335{col 49}{space 1}   -2.60{col 58}{space 3}0.009{col 66}{space 4}-3.909525{col 79}{space 3}-.5515834
{txt}{space 13}reb_polwing {c |}{col 26}{res}{space 2}  -.13507{col 38}{space 2} .3538581{col 49}{space 1}   -0.38{col 58}{space 3}0.703{col 66}{space 4} -.828619{col 79}{space 3}  .558479
{txt}{space 8}rebelsupport_dum {c |}{col 26}{res}{space 2} -.777887{col 38}{space 2} .4925162{col 49}{space 1}   -1.58{col 58}{space 3}0.114{col 66}{space 4}-1.743201{col 79}{space 3}  .187427
{txt}{space 10}govsupport_dum {c |}{col 26}{res}{space 2} .9031959{col 38}{space 2} .4910088{col 49}{space 1}    1.84{col 58}{space 3}0.066{col 66}{space 4}-.0591637{col 79}{space 3} 1.865556
{txt}{space 10}dem_dummy_nona {c |}{col 26}{res}{space 2} 1.690326{col 38}{space 2} .3945262{col 49}{space 1}    4.28{col 58}{space 3}0.000{col 66}{space 4}  .917069{col 79}{space 3} 2.463583
{txt}{space 24} {c |}
{space 2}indirect_learning_4cat {c |}
{space 22}0  {c |}{col 26}{res}{space 2}-.6129439{col 38}{space 2} .3263914{col 49}{space 1}   -1.88{col 58}{space 3}0.060{col 66}{space 4}-1.252659{col 79}{space 3} .0267714
{txt}{space 22}1  {c |}{col 26}{res}{space 2}-.0269022{col 38}{space 2}  .607622{col 49}{space 1}   -0.04{col 58}{space 3}0.965{col 66}{space 4}-1.217819{col 79}{space 3} 1.164015
{txt}{space 22}2  {c |}{col 26}{res}{space 2} .1110979{col 38}{space 2} .3106701{col 49}{space 1}    0.36{col 58}{space 3}0.721{col 66}{space 4}-.4978043{col 79}{space 3} .7200002
{txt}{space 24} {c |}
{space 23}t {c |}{col 26}{res}{space 2}-.1517754{col 38}{space 2} .0653846{col 49}{space 1}   -2.32{col 58}{space 3}0.020{col 66}{space 4}-.2799268{col 79}{space 3}-.0236239
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0051078{col 38}{space 2} .0029123{col 49}{space 1}    1.75{col 58}{space 3}0.079{col 66}{space 4}-.0006003{col 79}{space 3} .0108159
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000483{col 38}{space 2}  .000032{col 49}{space 1}   -1.51{col 58}{space 3}0.131{col 66}{space 4} -.000111{col 79}{space 3} .0000144
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.474232{col 38}{space 2} .8068575{col 49}{space 1}   -1.83{col 58}{space 3}0.068{col 66}{space 4}-3.055644{col 79}{space 3} .1071792
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 79 failures and 0 successes completely determined.{p_end}

{com}. estimates store a21
{txt}
{com}. 
.   
. * Table A6 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a18 a19 a20 a21 using tableA6.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A6: Additional Control Variables) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. + p<0.1, * p<0.05.) 
>   nonumbers mtitles("Model 1" "Model 2" "Model 3" "Model 4")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA6.rtf not found)
(output written to {browse  `"tableA6.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. 
. 
. *********************************************
. *********************************************
. *** Table A7: Alternative Model Selection ***
. *********************************************
. *********************************************
. 
. * Model 1 (rare events logit)
. relogit neg_DV battles_escalation3 battles_escalation2 weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3 , cl(dyadid)
{txt}(2,688 missing values generated)


Corrected logit estimates                             Number of obs ={res}     1484

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}battles_escalation3 {c |}{col 23}{res}{space 2}-.4618488{col 35}{space 2} .1615232{col 46}{space 1}   -2.86{col 55}{space 3}0.004{col 63}{space 4}-.7784284{col 76}{space 3}-.1452691
{txt}{space 2}battles_escalation2 {c |}{col 23}{res}{space 2}-.3981722{col 35}{space 2} .2353231{col 46}{space 1}   -1.69{col 55}{space 3}0.091{col 63}{space 4}-.8593969{col 76}{space 3} .0630526
{txt}weak_con_last_neg_dum {c |}{col 23}{res}{space 2}  .460721{col 35}{space 2} .1530105{col 46}{space 1}    3.01{col 55}{space 3}0.003{col 63}{space 4} .1608259{col 76}{space 3} .7606161
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0046056{col 35}{space 2} .1135449{col 46}{space 1}    0.04{col 55}{space 3}0.968{col 63}{space 4}-.2179383{col 76}{space 3} .2271495
{txt}{space 15}parity {c |}{col 23}{res}{space 2}   .52059{col 35}{space 2} .1738941{col 46}{space 1}    2.99{col 55}{space 3}0.003{col 63}{space 4} .1797639{col 76}{space 3} .8614161
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0480542{col 35}{space 2} .1486143{col 46}{space 1}    0.32{col 55}{space 3}0.746{col 63}{space 4}-.2432244{col 76}{space 3} .3393328
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0011695{col 35}{space 2} .0025891{col 46}{space 1}   -0.45{col 55}{space 3}0.651{col 63}{space 4} -.006244{col 76}{space 3} .0039051
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1740441{col 35}{space 2} .0464144{col 46}{space 1}   -3.75{col 55}{space 3}0.000{col 63}{space 4}-.2650148{col 76}{space 3}-.0830735
{txt}{space 19}t2 {c |}{col 23}{res}{space 2} .0046577{col 35}{space 2}  .002314{col 46}{space 1}    2.01{col 55}{space 3}0.044{col 63}{space 4} .0001223{col 76}{space 3}  .009193
{txt}{space 19}t3 {c |}{col 23}{res}{space 2}-.0000407{col 35}{space 2} .0000278{col 46}{space 1}   -1.47{col 55}{space 3}0.143{col 63}{space 4}-.0000951{col 76}{space 3} .0000137
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}-.9031074{col 35}{space 2} .4912621{col 46}{space 1}   -1.84{col 55}{space 3}0.066{col 63}{space 4}-1.865963{col 76}{space 3} .0597486
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a22
{txt}
{com}. 
. * Model 2 (rare events logit & third-party involvement)
. relogit neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3 , cl(dyadid)
{txt}(2,993 missing values generated)


Corrected logit estimates                             Number of obs ={res}     1179

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}battles_escalation3 {c |}{col 26}{res}{space 2}-1.240721{col 38}{space 2} .4091056{col 49}{space 1}   -3.03{col 58}{space 3}0.002{col 66}{space 4}-2.042554{col 79}{space 3}-.4388891
{txt}{space 5}battles_escalation2 {c |}{col 26}{res}{space 2}-.3067044{col 38}{space 2} .4791452{col 49}{space 1}   -0.64{col 58}{space 3}0.522{col 66}{space 4}-1.245812{col 79}{space 3} .6324029
{txt}{space 2}thirdparty_involvement {c |}{col 26}{res}{space 2} .5915605{col 38}{space 2} .5234127{col 49}{space 1}    1.13{col 58}{space 3}0.258{col 66}{space 4}-.4343094{col 79}{space 3}  1.61743
{txt}escalation_thirdparty_re {c |}{col 26}{res}{space 2} 1.243582{col 38}{space 2} .5015319{col 49}{space 1}    2.48{col 58}{space 3}0.013{col 66}{space 4}  .260598{col 79}{space 3} 2.226567
{txt}{space 2}nochange_thirdparty_re {c |}{col 26}{res}{space 2} .2670423{col 38}{space 2} .6737038{col 49}{space 1}    0.40{col 58}{space 3}0.692{col 66}{space 4}-1.053393{col 79}{space 3} 1.587478
{txt}{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2}  .430719{col 38}{space 2} .2047214{col 49}{space 1}    2.10{col 58}{space 3}0.035{col 66}{space 4} .0294723{col 79}{space 3} .8319656
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0944929{col 38}{space 2} .0950292{col 49}{space 1}   -0.99{col 58}{space 3}0.320{col 66}{space 4}-.2807467{col 79}{space 3} .0917609
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5147588{col 38}{space 2} .1915974{col 49}{space 1}    2.69{col 58}{space 3}0.007{col 66}{space 4} .1392347{col 79}{space 3} .8902828
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0971763{col 38}{space 2} .1728553{col 49}{space 1}   -0.56{col 58}{space 3}0.574{col 66}{space 4}-.4359665{col 79}{space 3} .2416139
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0007165{col 38}{space 2} .0022142{col 49}{space 1}   -0.32{col 58}{space 3}0.746{col 66}{space 4}-.0050562{col 79}{space 3} .0036232
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1563926{col 38}{space 2} .0526067{col 49}{space 1}   -2.97{col 58}{space 3}0.003{col 66}{space 4}-.2594999{col 79}{space 3}-.0532853
{txt}{space 22}t2 {c |}{col 26}{res}{space 2} .0043032{col 38}{space 2} .0024947{col 49}{space 1}    1.72{col 58}{space 3}0.085{col 66}{space 4}-.0005864{col 79}{space 3} .0091927
{txt}{space 22}t3 {c |}{col 26}{res}{space 2}-.0000359{col 38}{space 2}  .000028{col 49}{space 1}   -1.28{col 58}{space 3}0.200{col 66}{space 4}-.0000908{col 79}{space 3}  .000019
{txt}{space 19}_cons {c |}{col 26}{res}{space 2}-.9896829{col 38}{space 2} .6204515{col 49}{space 1}   -1.60{col 58}{space 3}0.111{col 66}{space 4}-2.205745{col 79}{space 3} .2263796
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a23
{txt}
{com}. 
. * Model 3 (Penalized ML)
. firthlogit neg_DV ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-419.42674}
rescale:{col 16}penalized log likelihood = {res:-419.42674}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-419.42674}  
Iteration 1:{space 3}penalized log likelihood = {res:  -372.655}  (not concave)
Iteration 2:{space 3}penalized log likelihood = {res:-364.17623}  (not concave)
Iteration 3:{space 3}penalized log likelihood = {res: -362.7272}  (not concave)
Iteration 4:{space 3}penalized log likelihood = {res:-362.33711}  
Iteration 5:{space 3}penalized log likelihood = {res:-361.97992}  
Iteration 6:{space 3}penalized log likelihood = {res:-361.85119}  
Iteration 7:{space 3}penalized log likelihood = {res: -361.8506}  
Iteration 8:{space 3}penalized log likelihood = {res: -361.8506}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}     72.92
{txt}Penalized log likelihood = {res} -361.8506{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               neg_DV{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2} -.406586{col 35}{space 2} .2569365{col 46}{space 1}   -1.58{col 55}{space 3}0.114{col 63}{space 4}-.9101722{col 76}{space 3} .0970002
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.4820307{col 35}{space 2} .2099022{col 46}{space 1}   -2.30{col 55}{space 3}0.022{col 63}{space 4}-.8934315{col 76}{space 3}-.0706299
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .4683451{col 35}{space 2} .1971727{col 46}{space 1}    2.38{col 55}{space 3}0.018{col 63}{space 4} .0818937{col 76}{space 3} .8547966
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.0075303{col 35}{space 2} .1238841{col 46}{space 1}   -0.06{col 55}{space 3}0.952{col 63}{space 4}-.2503387{col 76}{space 3} .2352782
{txt}{space 15}parity {c |}{col 23}{res}{space 2}  .547559{col 35}{space 2} .2899201{col 46}{space 1}    1.89{col 55}{space 3}0.059{col 63}{space 4} -.020674{col 76}{space 3} 1.115792
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0669584{col 35}{space 2} .1327926{col 46}{space 1}    0.50{col 55}{space 3}0.614{col 63}{space 4}-.1933103{col 76}{space 3} .3272271
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0011915{col 35}{space 2} .0020708{col 46}{space 1}   -0.58{col 55}{space 3}0.565{col 63}{space 4}-.0052501{col 76}{space 3} .0028672
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.0887084{col 35}{space 2} .0231647{col 46}{space 1}   -3.83{col 55}{space 3}0.000{col 63}{space 4}-.1341105{col 76}{space 3}-.0433063
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2}  .000733{col 35}{space 2} .0004822{col 46}{space 1}    1.52{col 55}{space 3}0.128{col 63}{space 4} -.000212{col 76}{space 3} .0016781
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-1.80e-06{col 35}{space 2} 2.13e-06{col 46}{space 1}   -0.84{col 55}{space 3}0.400{col 63}{space 4}-5.98e-06{col 76}{space 3} 2.39e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-1.205424{col 35}{space 2} .4134117{col 46}{space 1}   -2.92{col 55}{space 3}0.004{col 63}{space 4}-2.015696{col 76}{space 3}-.3951518
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a24
{txt}
{com}. 
. * Model 4 (Penalized ML & third-party involvement)
. firthlogit neg_DV ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t 
{res}
{txt}initial:{col 16}penalized log likelihood = {res:-318.68414}
rescale:{col 16}penalized log likelihood = {res:-318.68414}
{res}{txt}Iteration 0:{space 3}penalized log likelihood = {res:-318.68414}  
Iteration 1:{space 3}penalized log likelihood = {res:-273.80642}  
Iteration 2:{space 3}penalized log likelihood = {res:-259.71672}  
Iteration 3:{space 3}penalized log likelihood = {res:-259.16353}  
Iteration 4:{space 3}penalized log likelihood = {res:-259.14791}  
Iteration 5:{space 3}penalized log likelihood = {res:-259.14781}  
Iteration 6:{space 3}penalized log likelihood = {res:-259.14781}  
{res}
{txt}{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}     78.92
{txt}Penalized log likelihood = {res}-259.14781{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  neg_DV{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.3969343{col 38}{space 2} .4677642{col 49}{space 1}   -0.85{col 58}{space 3}0.396{col 66}{space 4}-1.313735{col 79}{space 3} .5198667
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.315585{col 38}{space 2} .5064683{col 49}{space 1}   -2.60{col 58}{space 3}0.009{col 66}{space 4}-2.308245{col 79}{space 3}-.3229252
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .5490221{col 38}{space 2} .3585996{col 49}{space 1}    1.53{col 58}{space 3}0.126{col 66}{space 4}-.1538202{col 79}{space 3} 1.251864
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .3390778{col 38}{space 2} .6008743{col 49}{space 1}    0.56{col 58}{space 3}0.573{col 66}{space 4}-.8386142{col 79}{space 3}  1.51677
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.311763{col 38}{space 2} .5698769{col 49}{space 1}    2.30{col 58}{space 3}0.021{col 66}{space 4} .1948244{col 79}{space 3} 2.428701
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4440803{col 38}{space 2} .2350302{col 49}{space 1}    1.89{col 58}{space 3}0.059{col 66}{space 4}-.0165705{col 79}{space 3}  .904731
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.1159033{col 38}{space 2} .1409804{col 49}{space 1}   -0.82{col 58}{space 3}0.411{col 66}{space 4}-.3922198{col 79}{space 3} .1604133
{txt}{space 18}parity {c |}{col 26}{res}{space 2}  .570528{col 38}{space 2} .3587075{col 49}{space 1}    1.59{col 58}{space 3}0.112{col 66}{space 4}-.1325259{col 79}{space 3} 1.273582
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0919461{col 38}{space 2} .1721344{col 49}{space 1}   -0.53{col 58}{space 3}0.593{col 66}{space 4}-.4293233{col 79}{space 3} .2454311
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0008405{col 38}{space 2} .0024044{col 49}{space 1}   -0.35{col 58}{space 3}0.727{col 66}{space 4} -.005553{col 79}{space 3} .0038721
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.0741453{col 38}{space 2} .0280826{col 49}{space 1}   -2.64{col 58}{space 3}0.008{col 66}{space 4}-.1291862{col 79}{space 3}-.0191044
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0007186{col 38}{space 2} .0006338{col 49}{space 1}    1.13{col 58}{space 3}0.257{col 66}{space 4}-.0005236{col 79}{space 3} .0019608
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-2.14e-06{col 38}{space 2} 3.17e-06{col 49}{space 1}   -0.68{col 58}{space 3}0.499{col 66}{space 4}-8.36e-06{col 79}{space 3} 4.08e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.203294{col 38}{space 2} .5596627{col 49}{space 1}   -2.15{col 58}{space 3}0.032{col 66}{space 4}-2.300213{col 79}{space 3}-.1063754
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a25
{txt}
{com}. 
. * Table A7 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a22 a23 a24 a25 using tableA7.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A7: Rare Event & Penalized ML Logistic Regression Results) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. + p<0.1, * p<0.05.) 
>   nonumbers mtitles("Rare Event" "Rare Event" "Penalized ML" "Penalized ML")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA7.rtf not found)
(output written to {browse  `"tableA7.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}.         
. 
. 
. ************************
. ************************
. *** Table A8: Mlogit ***
. ************************
. ************************
. use "BattlesBargains_ISQ_ReplicationData.dta", clear
{txt}
{com}. set more off
{txt}
{com}. 
. * Model 1: using cat3_DV2 (0=none, 1=negotiation or settlement/ceasefire, 2=victory/low activity)
. mlogit cat3_DV2 ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-597.76916}  
Iteration 1:{space 3}log pseudolikelihood = {res:-543.48485}  
Iteration 2:{space 3}log pseudolikelihood = {res:-531.86614}  
Iteration 3:{space 3}log pseudolikelihood = {res:-530.39695}  
Iteration 4:{space 3}log pseudolikelihood = {res:-529.56432}  
Iteration 5:{space 3}log pseudolikelihood = {res:-528.64841}  
Iteration 6:{space 3}log pseudolikelihood = {res:-527.47495}  
Iteration 7:{space 3}log pseudolikelihood = {res:-526.89997}  
Iteration 8:{space 3}log pseudolikelihood = {res:-526.81487}  
Iteration 9:{space 3}log pseudolikelihood = {res:-526.81385}  
Iteration 10:{space 2}log pseudolikelihood = {res:-526.81385}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}20{txt}){col 67}= {res}    275.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-526.81385{txt}{col 49}Pseudo R2{col 67}= {res}    0.1187

{txt}{ralign 87:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}             cat3_DV2{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                    {col 23}{txt}{c |}  (base outcome)
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                     {txt}{c |}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.4206819{col 35}{space 2} .2411155{col 46}{space 1}   -1.74{col 55}{space 3}0.081{col 63}{space 4}-.8932596{col 76}{space 3} .0518958
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.5424983{col 35}{space 2} .1609969{col 46}{space 1}   -3.37{col 55}{space 3}0.001{col 63}{space 4}-.8580464{col 76}{space 3}-.2269501
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .5098274{col 35}{space 2} .1493005{col 46}{space 1}    3.41{col 55}{space 3}0.001{col 63}{space 4} .2172038{col 76}{space 3} .8024509
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0341771{col 35}{space 2} .1112057{col 46}{space 1}    0.31{col 55}{space 3}0.759{col 63}{space 4} -.183782{col 76}{space 3} .2521362
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .5414347{col 35}{space 2} .1659508{col 46}{space 1}    3.26{col 55}{space 3}0.001{col 63}{space 4} .2161772{col 76}{space 3} .8666923
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0542569{col 35}{space 2} .1537836{col 46}{space 1}    0.35{col 55}{space 3}0.724{col 63}{space 4}-.2471534{col 76}{space 3} .3556673
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0021809{col 35}{space 2} .0027746{col 46}{space 1}   -0.79{col 55}{space 3}0.432{col 63}{space 4} -.007619{col 76}{space 3} .0032573
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1796342{col 35}{space 2} .0473172{col 46}{space 1}   -3.80{col 55}{space 3}0.000{col 63}{space 4}-.2723743{col 76}{space 3}-.0868941
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0057706{col 35}{space 2} .0024163{col 46}{space 1}    2.39{col 55}{space 3}0.017{col 63}{space 4} .0010348{col 76}{space 3} .0105064
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000613{col 35}{space 2} .0000305{col 46}{space 1}   -2.01{col 55}{space 3}0.045{col 63}{space 4}-.0001211{col 76}{space 3}-1.43e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.8857012{col 35}{space 2} .4933287{col 46}{space 1}   -1.80{col 55}{space 3}0.073{col 63}{space 4}-1.852608{col 76}{space 3} .0812052
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                     {txt}{c |}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.1797657{col 35}{space 2}  .492993{col 46}{space 1}   -0.36{col 55}{space 3}0.715{col 63}{space 4}-1.146014{col 76}{space 3} .7864828
{txt}{space 19}2  {c |}{col 23}{res}{space 2} -1.10393{col 35}{space 2} .6109922{col 46}{space 1}   -1.81{col 55}{space 3}0.071{col 63}{space 4}-2.301453{col 76}{space 3} .0935923
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2}-.9701762{col 35}{space 2} .6883902{col 46}{space 1}   -1.41{col 55}{space 3}0.159{col 63}{space 4}-2.319396{col 76}{space 3} .3790439
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.2588204{col 35}{space 2} .2269274{col 46}{space 1}   -1.14{col 55}{space 3}0.254{col 63}{space 4}-.7035899{col 76}{space 3} .1859491
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .6244732{col 35}{space 2}  .717382{col 46}{space 1}    0.87{col 55}{space 3}0.384{col 63}{space 4}-.7815698{col 76}{space 3} 2.030516
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2}  .120959{col 35}{space 2} .2740974{col 46}{space 1}    0.44{col 55}{space 3}0.659{col 63}{space 4}-.4162619{col 76}{space 3}   .65818
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0004617{col 35}{space 2} .0044864{col 46}{space 1}   -0.10{col 55}{space 3}0.918{col 63}{space 4}-.0092549{col 76}{space 3} .0083315
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.0534073{col 35}{space 2} .0525983{col 46}{space 1}   -1.02{col 55}{space 3}0.310{col 63}{space 4}-.1564981{col 76}{space 3} .0496834
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0016963{col 35}{space 2}   .00139{col 46}{space 1}    1.22{col 55}{space 3}0.222{col 63}{space 4}-.0010281{col 76}{space 3} .0044206
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000123{col 35}{space 2} 9.06e-06{col 46}{space 1}   -1.36{col 55}{space 3}0.173{col 63}{space 4}-.0000301{col 76}{space 3} 5.41e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-2.958048{col 35}{space 2} .7090152{col 46}{space 1}   -4.17{col 55}{space 3}0.000{col 63}{space 4}-4.347692{col 76}{space 3}-1.568403
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 59 observations completely determined.{txt}  Standard errors questionable.{p_end}

{com}. estimates store a26
{txt}
{com}. * Descriptives for text
. tab cat3_DV2 if e(sample)

{txt}Negotiation {c |}
         or {c |}
Termination {c |}
         DV {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}      1,313       88.48       88.48
{txt}          1 {c |}{res}        148        9.97       98.45
{txt}          2 {c |}{res}         23        1.55      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,484      100.00
{txt}
{com}. 
. * Model 2: using cat3_DV2
. mlogit cat3_DV2 ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-472.53376}  
Iteration 1:{space 3}log pseudolikelihood = {res:-421.27068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-405.05027}  
Iteration 3:{space 3}log pseudolikelihood = {res:-403.81945}  
Iteration 4:{space 3}log pseudolikelihood = {res:-403.17534}  
Iteration 5:{space 3}log pseudolikelihood = {res:-402.34155}  
Iteration 6:{space 3}log pseudolikelihood = {res:-401.48205}  
Iteration 7:{space 3}log pseudolikelihood = {res:-401.20827}  
Iteration 8:{space 3}log pseudolikelihood = {res:-401.19416}  
Iteration 9:{space 3}log pseudolikelihood = {res:-401.19411}  
Iteration 10:{space 2}log pseudolikelihood = {res:-401.19411}  
{res}
{txt}Multinomial logistic regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}26{txt}){col 67}= {res}    535.58
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-401.19411{txt}{col 49}Pseudo R2{col 67}= {res}    0.1510

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                cat3_DV2{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                       {col 26}{txt}{c |}  (base outcome)
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                        {txt}{c |}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.2074076{col 38}{space 2} .4713751{col 49}{space 1}   -0.44{col 58}{space 3}0.660{col 66}{space 4}-1.131286{col 79}{space 3} .7164706
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.259088{col 38}{space 2} .4385477{col 49}{space 1}   -2.87{col 58}{space 3}0.004{col 66}{space 4}-2.118626{col 79}{space 3}-.3995506
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .6178689{col 38}{space 2} .4861491{col 49}{space 1}    1.27{col 58}{space 3}0.204{col 66}{space 4}-.3349658{col 79}{space 3} 1.570704
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .0449791{col 38}{space 2} .6572404{col 49}{space 1}    0.07{col 58}{space 3}0.945{col 66}{space 4}-1.243188{col 79}{space 3} 1.333147
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.161417{col 38}{space 2} .5128115{col 49}{space 1}    2.26{col 58}{space 3}0.024{col 66}{space 4} .1563247{col 79}{space 3} 2.166509
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .5168147{col 38}{space 2} .2099433{col 49}{space 1}    2.46{col 58}{space 3}0.014{col 66}{space 4} .1053333{col 79}{space 3}  .928296
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0552117{col 38}{space 2} .0983675{col 49}{space 1}   -0.56{col 58}{space 3}0.575{col 66}{space 4}-.2480084{col 79}{space 3} .1375851
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5715072{col 38}{space 2} .2014505{col 49}{space 1}    2.84{col 58}{space 3}0.005{col 66}{space 4} .1766714{col 79}{space 3} .9663429
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0961584{col 38}{space 2} .1764646{col 49}{space 1}   -0.54{col 58}{space 3}0.586{col 66}{space 4}-.4420226{col 79}{space 3} .2497058
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0022499{col 38}{space 2} .0023722{col 49}{space 1}   -0.95{col 58}{space 3}0.343{col 66}{space 4}-.0068993{col 79}{space 3} .0023995
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1606305{col 38}{space 2} .0544728{col 49}{space 1}   -2.95{col 58}{space 3}0.003{col 66}{space 4}-.2673952{col 79}{space 3}-.0538658
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0054172{col 38}{space 2} .0026388{col 49}{space 1}    2.05{col 58}{space 3}0.040{col 66}{space 4} .0002453{col 79}{space 3}  .010589
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2} -.000057{col 38}{space 2} .0000312{col 49}{space 1}   -1.83{col 58}{space 3}0.068{col 66}{space 4}-.0001181{col 79}{space 3} 4.17e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.9860026{col 38}{space 2} .6172696{col 49}{space 1}   -1.60{col 58}{space 3}0.110{col 66}{space 4}-2.195829{col 79}{space 3} .2238235
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                        {txt}{c |}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.4685011{col 38}{space 2} .5572029{col 49}{space 1}   -0.84{col 58}{space 3}0.400{col 66}{space 4}-1.560599{col 79}{space 3} .6235965
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-1.646683{col 38}{space 2} .9108883{col 49}{space 1}   -1.81{col 58}{space 3}0.071{col 66}{space 4}-3.431991{col 79}{space 3} .1386254
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2}-.7963506{col 38}{space 2}  .781213{col 49}{space 1}   -1.02{col 58}{space 3}0.308{col 66}{space 4}  -2.3275{col 79}{space 3} .7347988
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2}  1.60205{col 38}{space 2} 1.440239{col 49}{space 1}    1.11{col 58}{space 3}0.266{col 66}{space 4}-1.220767{col 79}{space 3} 4.424866
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} 1.624967{col 38}{space 2} 1.350955{col 49}{space 1}    1.20{col 58}{space 3}0.229{col 66}{space 4}-1.022856{col 79}{space 3} 4.272791
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2}-1.271799{col 38}{space 2} .8940106{col 49}{space 1}   -1.42{col 58}{space 3}0.155{col 66}{space 4}-3.024028{col 79}{space 3} .4804294
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2} -.338721{col 38}{space 2} .2360174{col 49}{space 1}   -1.44{col 58}{space 3}0.151{col 66}{space 4}-.8013066{col 79}{space 3} .1238646
{txt}{space 18}parity {c |}{col 26}{res}{space 2} 1.161817{col 38}{space 2} .9189378{col 49}{space 1}    1.26{col 58}{space 3}0.206{col 66}{space 4}-.6392679{col 79}{space 3} 2.962902
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0036304{col 38}{space 2}  .344794{col 49}{space 1}   -0.01{col 58}{space 3}0.992{col 66}{space 4}-.6794143{col 79}{space 3} .6721535
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0011274{col 38}{space 2} .0056055{col 49}{space 1}   -0.20{col 58}{space 3}0.841{col 66}{space 4}-.0121139{col 79}{space 3}  .009859
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.0328481{col 38}{space 2} .0525453{col 49}{space 1}   -0.63{col 58}{space 3}0.532{col 66}{space 4}-.1358351{col 79}{space 3} .0701388
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0011782{col 38}{space 2} .0012888{col 49}{space 1}    0.91{col 58}{space 3}0.361{col 66}{space 4}-.0013477{col 79}{space 3} .0037042
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-8.97e-06{col 38}{space 2} 8.04e-06{col 49}{space 1}   -1.12{col 58}{space 3}0.265{col 66}{space 4}-.0000247{col 79}{space 3} 6.79e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-2.555813{col 38}{space 2} .9943592{col 49}{space 1}   -2.57{col 58}{space 3}0.010{col 66}{space 4}-4.504721{col 79}{space 3}-.6069049
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: 28 observations completely determined.{txt}  Standard errors questionable.{p_end}

{com}. estimates store a27
{txt}
{com}. 
. * Table A8 *
. #delimit;
{txt}delimiter now ;
{com}. esttab a26 a27 using tableA8.rtf, replace
>   stats(N) cells("b(star fmt(3))" se(par fmt(3))) 
>   modelwidth(7) se label nogaps nonotes title(Table A8: Multinomial Logistic Regression Results) 
>   addnote(Note: Standard errors, clustered on dyad ID, in parentheses. + p<0.1, * p<0.05.) 
>   nonumbers mtitles("Negotiations" "Settlement/Ceasefire" "Victory/Low Activity")  
>   star(+ 0.1 * 0.05) ;
{res}{txt}(note: file tableA8.rtf not found)
(output written to {browse  `"tableA8.rtf"'})

{com}. #delimit cr
{txt}delimiter now cr
{com}. 
. * SI Footnote 4
. mprobit cat3_DV2 ib0.battles_escalation weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-530.91302}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-527.30828}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-526.34843}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-526.33964}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-526.33963}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}     1,484
{txt}{col 49}Wald chi2({res}20{txt}){col 67}= {res}    273.61
{txt}Log pseudolikelihood = {res}-526.33963{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 87:(Std. Err. adjusted for {res:42} clusters in dyadid)}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}             cat3_DV2{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                    {col 23}{txt}{c |}  (base outcome)
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                     {txt}{c |}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.3372461{col 35}{space 2} .1744631{col 46}{space 1}   -1.93{col 55}{space 3}0.053{col 63}{space 4}-.6791875{col 76}{space 3} .0046953
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.4324235{col 35}{space 2} .1198701{col 46}{space 1}   -3.61{col 55}{space 3}0.000{col 63}{space 4}-.6673645{col 76}{space 3}-.1974825
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2} .3916203{col 35}{space 2} .1119796{col 46}{space 1}    3.50{col 55}{space 3}0.000{col 63}{space 4} .1721443{col 76}{space 3} .6110962
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2} .0268136{col 35}{space 2} .0806617{col 46}{space 1}    0.33{col 55}{space 3}0.740{col 63}{space 4}-.1312805{col 76}{space 3} .1849077
{txt}{space 15}parity {c |}{col 23}{res}{space 2} .4328957{col 35}{space 2} .1276741{col 46}{space 1}    3.39{col 55}{space 3}0.001{col 63}{space 4}  .182659{col 76}{space 3} .6831324
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0391266{col 35}{space 2} .1147589{col 46}{space 1}    0.34{col 55}{space 3}0.733{col 63}{space 4}-.1857966{col 76}{space 3} .2640498
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0016243{col 35}{space 2} .0020078{col 46}{space 1}   -0.81{col 55}{space 3}0.419{col 63}{space 4}-.0055596{col 76}{space 3}  .002311
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.1287545{col 35}{space 2} .0337418{col 46}{space 1}   -3.82{col 55}{space 3}0.000{col 63}{space 4}-.1948873{col 76}{space 3}-.0626218
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2} .0040049{col 35}{space 2} .0016546{col 46}{space 1}    2.42{col 55}{space 3}0.015{col 63}{space 4}  .000762{col 76}{space 3} .0072478
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-.0000404{col 35}{space 2} .0000197{col 46}{space 1}   -2.05{col 55}{space 3}0.041{col 63}{space 4}-.0000791{col 76}{space 3}-1.70e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}-.8105442{col 35}{space 2} .3593699{col 46}{space 1}   -2.26{col 55}{space 3}0.024{col 63}{space 4}-1.514896{col 76}{space 3}-.1061922
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                     {txt}{c |}
{space 3}battles_escalation {c |}
{space 19}1  {c |}{col 23}{res}{space 2}-.1214567{col 35}{space 2} .2687648{col 46}{space 1}   -0.45{col 55}{space 3}0.651{col 63}{space 4} -.648226{col 76}{space 3} .4053127
{txt}{space 19}2  {c |}{col 23}{res}{space 2}-.6313057{col 35}{space 2}  .304998{col 46}{space 1}   -2.07{col 55}{space 3}0.038{col 63}{space 4}-1.229091{col 76}{space 3}-.0335207
{txt}{space 21} {c |}
weak_con_last_neg_dum {c |}{col 23}{res}{space 2}-.4633256{col 35}{space 2} .3233461{col 46}{space 1}   -1.43{col 55}{space 3}0.152{col 63}{space 4}-1.097072{col 76}{space 3} .1704212
{txt}{space 9}str_centcomm {c |}{col 23}{res}{space 2}-.1456059{col 35}{space 2} .1196706{col 46}{space 1}   -1.22{col 55}{space 3}0.224{col 63}{space 4} -.380156{col 76}{space 3} .0889441
{txt}{space 15}parity {c |}{col 23}{res}{space 2}  .372477{col 35}{space 2} .3721713{col 46}{space 1}    1.00{col 55}{space 3}0.317{col 63}{space 4}-.3569653{col 76}{space 3} 1.101919
{txt}{space 13}groupnum {c |}{col 23}{res}{space 2} .0739773{col 35}{space 2} .1575344{col 46}{space 1}    0.47{col 55}{space 3}0.639{col 63}{space 4}-.2347844{col 76}{space 3}  .382739
{txt}{space 10}dyad_months {c |}{col 23}{res}{space 2}-.0004676{col 35}{space 2} .0024684{col 46}{space 1}   -0.19{col 55}{space 3}0.850{col 63}{space 4}-.0053055{col 76}{space 3} .0043703
{txt}{space 20}t {c |}{col 23}{res}{space 2}-.0427894{col 35}{space 2} .0300353{col 46}{space 1}   -1.42{col 55}{space 3}0.154{col 63}{space 4}-.1016576{col 76}{space 3} .0160787
{txt}{space 21} {c |}
{space 14}c.t#c.t {c |}{col 23}{res}{space 2}  .001202{col 35}{space 2} .0008076{col 46}{space 1}    1.49{col 55}{space 3}0.137{col 63}{space 4}-.0003809{col 76}{space 3} .0027849
{txt}{space 21} {c |}
{space 10}c.t#c.t#c.t {c |}{col 23}{res}{space 2}-8.45e-06{col 35}{space 2} 5.38e-06{col 46}{space 1}   -1.57{col 55}{space 3}0.116{col 63}{space 4} -.000019{col 76}{space 3} 2.08e-06
{txt}{space 21} {c |}
{space 16}_cons {c |}{col 23}{res}{space 2}  -2.1002{col 35}{space 2} .4158572{col 46}{space 1}   -5.05{col 55}{space 3}0.000{col 63}{space 4}-2.915265{col 76}{space 3}-1.285134
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. mprobit cat3_DV2 ib0.battles_escalation##i.thirdparty_involvement weak_con_last_neg_dum str_centcomm parity groupnum dyad_months c.t##c.t##c.t, cl(dyadid)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-403.53124}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-400.95863}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-399.96552}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-399.94991}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res: -399.9499}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}     1,179
{txt}{col 49}Wald chi2({res}26{txt}){col 67}= {res}    457.53
{txt}Log pseudolikelihood = {res} -399.9499{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 90:(Std. Err. adjusted for {res:35} clusters in dyadid)}
{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}    Robust
{col 1}                cat3_DV2{col 26}{c |}      Coef.{col 38}   Std. Err.{col 50}      z{col 58}   P>|z|{col 66}     [95% Con{col 79}f. Interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                       {col 26}{txt}{c |}  (base outcome)
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                        {txt}{c |}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.1632927{col 38}{space 2} .3325954{col 49}{space 1}   -0.49{col 58}{space 3}0.623{col 66}{space 4}-.8151676{col 79}{space 3} .4885823
{txt}{space 22}2  {c |}{col 26}{res}{space 2} -.856163{col 38}{space 2} .2881442{col 49}{space 1}   -2.97{col 58}{space 3}0.003{col 66}{space 4}-1.420915{col 79}{space 3}-.2914108
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2} .4849553{col 38}{space 2} .3357304{col 49}{space 1}    1.44{col 58}{space 3}0.149{col 66}{space 4}-.1730641{col 79}{space 3} 1.142975
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .0327731{col 38}{space 2} .4760621{col 49}{space 1}    0.07{col 58}{space 3}0.945{col 66}{space 4}-.9002914{col 79}{space 3} .9658377
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} .7742468{col 38}{space 2} .3511093{col 49}{space 1}    2.21{col 58}{space 3}0.027{col 66}{space 4} .0860853{col 79}{space 3} 1.462408
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2} .4130858{col 38}{space 2} .1740986{col 49}{space 1}    2.37{col 58}{space 3}0.018{col 66}{space 4} .0718589{col 79}{space 3} .7543127
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.0517087{col 38}{space 2} .0728975{col 49}{space 1}   -0.71{col 58}{space 3}0.478{col 66}{space 4}-.1945851{col 79}{space 3} .0911677
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .5031617{col 38}{space 2} .1687662{col 49}{space 1}    2.98{col 58}{space 3}0.003{col 66}{space 4} .1723859{col 79}{space 3} .8339374
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0685307{col 38}{space 2} .1255406{col 49}{space 1}   -0.55{col 58}{space 3}0.585{col 66}{space 4}-.3145858{col 79}{space 3} .1775243
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0016928{col 38}{space 2} .0017246{col 49}{space 1}   -0.98{col 58}{space 3}0.326{col 66}{space 4} -.005073{col 79}{space 3} .0016875
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.1159962{col 38}{space 2} .0390121{col 49}{space 1}   -2.97{col 58}{space 3}0.003{col 66}{space 4}-.1924585{col 79}{space 3}-.0395339
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0037923{col 38}{space 2} .0018631{col 49}{space 1}    2.04{col 58}{space 3}0.042{col 66}{space 4} .0001408{col 79}{space 3} .0074439
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-.0000383{col 38}{space 2} .0000216{col 49}{space 1}   -1.77{col 58}{space 3}0.076{col 66}{space 4}-.0000807{col 79}{space 3} 4.02e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-.8898613{col 38}{space 2} .4422689{col 49}{space 1}   -2.01{col 58}{space 3}0.044{col 66}{space 4}-1.756692{col 79}{space 3}-.0230302
{txt}{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                        {txt}{c |}
{space 6}battles_escalation {c |}
{space 22}1  {c |}{col 26}{res}{space 2}-.2742009{col 38}{space 2} .2857645{col 49}{space 1}   -0.96{col 58}{space 3}0.337{col 66}{space 4} -.834289{col 79}{space 3} .2858872
{txt}{space 22}2  {c |}{col 26}{res}{space 2}-.9618093{col 38}{space 2} .4460923{col 49}{space 1}   -2.16{col 58}{space 3}0.031{col 66}{space 4}-1.836134{col 79}{space 3}-.0874843
{txt}{space 24} {c |}
1.thirdparty_involvement {c |}{col 26}{res}{space 2}-.3548231{col 38}{space 2} .3669319{col 49}{space 1}   -0.97{col 58}{space 3}0.334{col 66}{space 4}-1.073996{col 79}{space 3} .3643501
{txt}{space 24} {c |}
{space 6}battles_escalation#{c |}
{space 2}thirdparty_involvement {c |}
{space 20}1 1  {c |}{col 26}{res}{space 2} .8882358{col 38}{space 2} .6747913{col 49}{space 1}    1.32{col 58}{space 3}0.188{col 66}{space 4}-.4343307{col 79}{space 3} 2.210802
{txt}{space 20}2 1  {c |}{col 26}{res}{space 2} .9401492{col 38}{space 2} .6562525{col 49}{space 1}    1.43{col 58}{space 3}0.152{col 66}{space 4} -.346082{col 79}{space 3}  2.22638
{txt}{space 24} {c |}
{space 3}weak_con_last_neg_dum {c |}{col 26}{res}{space 2}-.6230685{col 38}{space 2} .3860622{col 49}{space 1}   -1.61{col 58}{space 3}0.107{col 66}{space 4}-1.379737{col 79}{space 3} .1335996
{txt}{space 12}str_centcomm {c |}{col 26}{res}{space 2}-.1886441{col 38}{space 2} .1192035{col 49}{space 1}   -1.58{col 58}{space 3}0.114{col 66}{space 4}-.4222786{col 79}{space 3} .0449904
{txt}{space 18}parity {c |}{col 26}{res}{space 2} .6569518{col 38}{space 2} .4641326{col 49}{space 1}    1.42{col 58}{space 3}0.157{col 66}{space 4}-.2527314{col 79}{space 3} 1.566635
{txt}{space 16}groupnum {c |}{col 26}{res}{space 2}-.0534274{col 38}{space 2} .1667221{col 49}{space 1}   -0.32{col 58}{space 3}0.749{col 66}{space 4}-.3801966{col 79}{space 3} .2733419
{txt}{space 13}dyad_months {c |}{col 26}{res}{space 2}-.0009991{col 38}{space 2} .0029107{col 49}{space 1}   -0.34{col 58}{space 3}0.731{col 66}{space 4}-.0067039{col 79}{space 3} .0047057
{txt}{space 23}t {c |}{col 26}{res}{space 2}-.0280567{col 38}{space 2} .0293065{col 49}{space 1}   -0.96{col 58}{space 3}0.338{col 66}{space 4}-.0854965{col 79}{space 3}  .029383
{txt}{space 24} {c |}
{space 17}c.t#c.t {c |}{col 26}{res}{space 2} .0008638{col 38}{space 2} .0007558{col 49}{space 1}    1.14{col 58}{space 3}0.253{col 66}{space 4}-.0006175{col 79}{space 3} .0023451
{txt}{space 24} {c |}
{space 13}c.t#c.t#c.t {c |}{col 26}{res}{space 2}-6.33e-06{col 38}{space 2} 4.91e-06{col 49}{space 1}   -1.29{col 58}{space 3}0.197{col 66}{space 4} -.000016{col 79}{space 3} 3.29e-06
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}-1.826271{col 38}{space 2} .5046283{col 49}{space 1}   -3.62{col 58}{space 3}0.000{col 66}{space 4}-2.815324{col 79}{space 3}-.8372178
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. **************************************************
. **************************************************
. *** Figure A5: Baseline Hazard of Negotiations ***
. **************************************************
. **************************************************
. 
. * Figure A5a: Predicted Probabilities across Time Model 1 *
. set seed 11235
{txt}
{com}. estsimp logit neg_DV battles_escalation2 battles_escalation3 weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3 , cl(dyadid)

{txt}Iteration 0:   log pseudolikelihood = {res}-468.20555
{txt}Iteration 1:   log pseudolikelihood = {res}-419.55494
{txt}Iteration 2:   log pseudolikelihood = {res}-409.23659
{txt}Iteration 3:   log pseudolikelihood = {res}-407.89146
{txt}Iteration 4:   log pseudolikelihood = {res}-407.50159
{txt}Iteration 5:   log pseudolikelihood = {res}-407.19794
{txt}Iteration 6:   log pseudolikelihood = {res}-406.76688
{txt}Iteration 7:   log pseudolikelihood = {res}-406.08812
{txt}Iteration 8:   log pseudolikelihood = {res}  -405.365
{txt}Iteration 9:   log pseudolikelihood = {res}-404.93301
{txt}Iteration 10:  log pseudolikelihood = {res}-404.77104
{txt}Iteration 11:  log pseudolikelihood = {res}-404.74944
{txt}Iteration 12:  log pseudolikelihood = {res}-404.74907
{txt}Iteration 13:  log pseudolikelihood = {res}-404.74907

{txt}Logistic regression                               Number of obs   = {res}      1484
                                                  {txt}Wald chi2({res}10{txt})   = {res}    123.13
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log pseudolikelihood = {res}-404.74907                 {txt}Pseudo R2       = {res}    0.1355

                                {txt}(Std. Err. adjusted for {res}42{txt} clusters in dyadid)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
      neg_DV {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
battles_es~2 {c |}  {res}-.4192942   .2370674    -1.77   0.077    -.8839377    .0453493
{txt}battles_es~3 {c |}  {res}-.4683414   .1627205    -2.88   0.004    -.7872677   -.1494152
{txt}weak_con_l~m {c |}  {res} .4653051   .1541447     3.02   0.003     .1631871    .7674232
{txt}str_centcomm {c |}  {res} .0029436   .1143865     0.03   0.979    -.2212499    .2271372
      {txt}parity {c |}  {res} .5161277    .175183     2.95   0.003     .1727753    .8594801
    {txt}groupnum {c |}  {res}  .046643   .1497159     0.31   0.755    -.2467947    .3400807
 {txt}dyad_months {c |}  {res}-.0012904   .0026083    -0.49   0.621    -.0064026    .0038218
           {txt}t {c |}  {res}-.1895825   .0467585    -4.05   0.000    -.2812274   -.0979375
          {txt}t2 {c |}  {res} .0056347   .0023312     2.42   0.016     .0010657    .0102037
          {txt}t3 {c |}  {res}-.0000562    .000028    -2.01   0.045     -.000111   -1.35e-06
       {txt}_cons {c |}  {res}-.8649248   .4949035    -1.75   0.081    -1.834918    .1050682
{txt}{hline 13}{c BT}{hline 64}
Note: 112 failures and 0 successes completely determined.

{res}Simulating main parameters.  Please wait....
% of simulations completed: 9% 18% 27% 36% 45% 54% 63% 72% 81% 90% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11
{txt}
{com}. gen plo=.
{txt}(4,172 missing values generated)

{com}. gen phi=.
{txt}(4,172 missing values generated)

{com}. gen pmean=.
{txt}(4,172 missing values generated)

{com}. gen timeaxis=_n+1 in 1/81
{txt}(4,091 missing values generated)

{com}. local a=2
{txt}
{com}. while `a'<=82{c -(}
{txt}  2{com}. setx (t) `a' (t2) `a'^2 (t3) `a'^3
{txt}  3{com}. simqi, prval(1) genpr(neg_temp)
{txt}  4{com}. _pctile neg_temp, p(2.5, 50, 97.5)
{txt}  5{com}. replace plo=r(r1) if timeaxis==`a'
{txt}  6{com}. replace phi=r(r3) if timeaxis==`a'
{txt}  7{com}. replace pmean=r(r2) if timeaxis==`a'
{txt}  8{com}. drop neg_temp
{txt}  9{com}. local a= `a'+6
{txt} 10{com}. {c )-}

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .2392636      .080713     .1098529    .4217566

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .1224975       .04437     .0545275    .2291571

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0774655     .0301024     .0339925    .1496584

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0600406     .0251126     .0247757    .1204696

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0545155     .0256501     .0196052     .118986

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0545805     .0300235     .0164389    .1312272

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0560436     .0358812     .0139134    .1530401

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0547008     .0396225     .0116465    .1662104

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0475017     .0384433     .0085488    .1488173

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0354343     .0341226     .0049057    .1283668

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0236728     .0334675     .0013752    .1097099

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0167778      .042702     .0001942    .1208037

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0155534     .0634134     9.89e-06    .1433606

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0180479     .0908492     1.82e-07    .1708361

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. sort timeaxis
{txt}
{com}. graph twoway (connected pmean timeaxis, lw(medium) lcolor(black) ms(point) title("Model 1") graphregion(color(white)) ///
>      ytitle("Predicted Probability of Negotiations" " ") ///
>      xtitle("Months Since Previous Negotiations Ended" " ") ///
>      xtick(2(10)82) xlabel(2(10)82)  legend(off) ylabel(0(.1).53)) ///
>          (connected plo timeaxis, lw(thin) lcolor(gs10) ms(none)) ///
>          (connected phi timeaxis, lw(thin) lcolor(gs10) ms(none) saving(figureA5a.gph, replace))
{res}{txt}(note: file figureA5a.gph not found)
{res}{txt}(file figureA5a.gph saved)

{com}. 
. 
.   
. * Figure A5b: Predicted Probabilities across Time Model 2 *
. drop b1-b11
{txt}
{com}. estsimp logit neg_DV battles_escalation3 battles_escalation2 thirdparty_involvement escalation_thirdparty_re nochange_thirdparty_re weak_con_last_neg_dum str_centcomm parity groupnum dyad_months t t2 t3 , cl(dyadid)

{txt}Iteration 0:   log pseudolikelihood = {res}-367.88362
{txt}Iteration 1:   log pseudolikelihood = {res}-320.29006
{txt}Iteration 2:   log pseudolikelihood = {res}-306.82076
{txt}Iteration 3:   log pseudolikelihood = {res}-305.55259
{txt}Iteration 4:   log pseudolikelihood = {res}-305.15662
{txt}Iteration 5:   log pseudolikelihood = {res}-304.74774
{txt}Iteration 6:   log pseudolikelihood = {res}-304.22384
{txt}Iteration 7:   log pseudolikelihood = {res}-303.61377
{txt}Iteration 8:   log pseudolikelihood = {res}-303.19232
{txt}Iteration 9:   log pseudolikelihood = {res}-303.03115
{txt}Iteration 10:  log pseudolikelihood = {res}-303.00655
{txt}Iteration 11:  log pseudolikelihood = {res}-303.00598
{txt}Iteration 12:  log pseudolikelihood = {res}-303.00598

{txt}Logistic regression                               Number of obs   = {res}      1179
                                                  {txt}Wald chi2({res}13{txt})   = {res}    167.15
                                                  {txt}Prob > chi2     = {res}    0.0000
{txt}Log pseudolikelihood = {res}-303.00598                 {txt}Pseudo R2       = {res}    0.1764

                                {txt}(Std. Err. adjusted for {res}35{txt} clusters in dyadid)
{hline 13}{c TT}{hline 64}
             {c |}               Robust
      neg_DV {c |}      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
{hline 13}{c +}{hline 64}
battles_es~3 {c |}  {res}-1.281886   .4139636    -3.10   0.002     -2.09324   -.4705328
{txt}battles_es~2 {c |}  {res} -.323862   .4848348    -0.67   0.504    -1.274121    .6263967
{txt}thirdparty~t {c |}  {res} .6292978   .5296279     1.19   0.235    -.4087538    1.667349
{txt}escalation~e {c |}  {res} 1.276698   .5074873     2.52   0.012      .282041    2.271355
{txt}nochange_t~e {c |}  {res} .2502054   .6817037     0.37   0.714    -1.085909     1.58632
{txt}weak_con_l~m {c |}  {res}  .443358   .2071524     2.14   0.032     .0373468    .8493692
{txt}str_centcomm {c |}  {res}-.0990788   .0961576    -1.03   0.303    -.2875442    .0893867
      {txt}parity {c |}  {res} .5090595   .1938726     2.63   0.009     .1290762    .8890427
    {txt}groupnum {c |}  {res}-.1024433   .1749079    -0.59   0.558    -.4452565    .2403698
 {txt}dyad_months {c |}  {res}-.0008678   .0022405    -0.39   0.699     -.005259    .0035234
           {txt}t {c |}  {res}-.1755702   .0532314    -3.30   0.001    -.2799018   -.0712385
          {txt}t2 {c |}  {res}   .00544   .0025243     2.16   0.031     .0004924    .0103876
          {txt}t3 {c |}  {res} -.000053   .0000283    -1.87   0.062    -.0001085    2.57e-06
       {txt}_cons {c |}  {res}-.9683096    .627819    -1.54   0.123    -2.198812     .262193
{txt}{hline 13}{c BT}{hline 64}
Note: 81 failures and 0 successes completely determined.

{res}Simulating main parameters.  Please wait....
% of simulations completed: 7% 14% 21% 28% 35% 42% 50% 57% 64% 71% 78% 85% 92% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9 b10 b11 b12 b13 b14
{txt}
{com}. gen plo3=.
{txt}(4,172 missing values generated)

{com}. gen phi3=.
{txt}(4,172 missing values generated)

{com}. gen pmean3=.
{txt}(4,172 missing values generated)

{com}. gen timeaxis3=_n+1 in 1/81
{txt}(4,091 missing values generated)

{com}. local a=2
{txt}
{com}. while `a'<=82{c -(}
{txt}  2{com}. setx (t) `a' (t2) `a'^2 (t3) `a'^3
{txt}  3{com}. simqi, prval(1) genpr(neg_temp)
{txt}  4{com}. _pctile neg_temp, p(2.5, 50, 97.5)
{txt}  5{com}. replace plo3=r(r1) if timeaxis3==`a'
{txt}  6{com}. replace phi3=r(r3) if timeaxis3==`a'
{txt}  7{com}. replace pmean3=r(r2) if timeaxis3==`a'
{txt}  8{com}. drop neg_temp
{txt}  9{com}. local a= `a'+6
{txt} 10{com}. {c )-}

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .2337274     .1043022     .0812276    .4707502

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .1298545     .0661453     .0402181    .2832694

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0878434     .0472798     .0266986    .2073469

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0713821     .0381458     .0208721    .1676335

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0671977     .0355428     .0189229    .1553353

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0698799     .0386997     .0197245    .1652735

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0754927     .0462603       .01813    .1924143

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0790086     .0541892     .0165349    .2256539

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0750104     .0572505     .0128926    .2365733

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0615568      .054116      .008504    .2108642

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0434504     .0504746     .0037364    .1786515

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0287191      .052091     .0007424    .1568822

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0211926     .0604993     .0000611    .1714001

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
              Pr(neg_DV=1) |  {res} .0202361     .0802469     1.68e-06    .1985471

Simqi generated the following new variable(s): neg_temp
{txt}(1 real change made)
(1 real change made)
(1 real change made)

{com}. sort timeaxis3
{txt}
{com}. graph twoway (connected pmean3 timeaxis3, lw(medium) lcolor(black) ms(point) title("Model 2") graphregion(color(white)) ///
>      ytitle(" ") ///
>      xtitle("Months Since Previous Negotiations Ended" " ") ///
>          xtick(2(10)82) xlabel(2(10)82)  legend(off) ylabel(0(.1).53)) ///
>          (connected plo3 timeaxis3, lw(thin) lcolor(gs10) ms(none)) ///
>          (connected phi3 timeaxis3, lw(thin) lcolor(gs10) ms(none) saving(figureA5b.gph, replace))
{res}{txt}(note: file figureA5b.gph not found)
{res}{txt}(file figureA5b.gph saved)

{com}. 
. #delimit;
{txt}delimiter now ;
{com}. graph combine figureA5a.gph figureA5b.gph ,
>   col(3)
>   title("Figure A5: Predicted Probability of Negotiations over Time", size(med))
>   subtitle("", size(medsmall))
>   graphregion(color(white))
>   note("Note: 95% Confidence Intervals reported.", size(small)) 
>   saving(figureA5.gph, replace)  ;
{res}{p 0 4 2}
{txt}(note:  named style
med not found in class
gsize,  default attributes used)
{p_end}
{res}{txt}(note: file figureA5.gph not found)
{res}{txt}(file figureA5.gph saved)

{com}. #delimit cr      
{txt}delimiter now cr
{com}.          
. drop b1-b14 plo plo3 phi phi3 pmean pmean3 timeaxis timeaxis3
{txt}
{com}. clear
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/aprorok/Dropbox/Ceasefires Grant with Alyssa/LHF/Data & Analyses/Replication Files & Final Figures to Upload/BattlesBargains_ISQ_ReplicationLog.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 8 Jan 2021, 20:45:36
{txt}{.-}
{smcl}
{txt}{sf}{ul off}